{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "NVT Simulation.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/google/jax-md/blob/simulation_refactor_2/notebooks/nvt_simulation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kNDQ02qlDnNW",
        "cellView": "form"
      },
      "source": [
        "#@title Imports & Utils\n",
        "\n",
        "!pip install jax-md\n",
        "\n",
        "import numpy as onp\n",
        "\n",
        "from jax.config import config ; config.update('jax_enable_x64', True)\n",
        "import jax.numpy as np\n",
        "from jax import random\n",
        "from jax import jit\n",
        "from jax import lax\n",
        "from jax import ops\n",
        "\n",
        "import time\n",
        "\n",
        "from jax_md import space, smap, energy, minimize, quantity, simulate\n",
        "\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "  \n",
        "sns.set_style(style='white')\n",
        "\n",
        "def format_plot(x, y):  \n",
        "  plt.xlabel(x, fontsize=20)\n",
        "  plt.ylabel(y, fontsize=20)\n",
        "  \n",
        "def finalize_plot(shape=(1, 1)):\n",
        "  plt.gcf().set_size_inches(\n",
        "    shape[0] * 1.5 * plt.gcf().get_size_inches()[1], \n",
        "    shape[1] * 1.5 * plt.gcf().get_size_inches()[1])\n",
        "  plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uxCDg0ioWh70"
      },
      "source": [
        "# Constant Temperature Simulation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dFK0Dew5MwXt"
      },
      "source": [
        "Here we demonstrate some code to run a simulation at in the NVT ensemble. We start off by setting up some parameters of the simulation. This will include a temperature schedule that will start off at a high temperature and then instantaneously quench to a lower temperature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGdrjCj1Wm9J"
      },
      "source": [
        "N = 400\n",
        "dimension = 2\n",
        "box_size = quantity.box_size_at_number_density(N, 0.8, 2)\n",
        "dt = 5e-3\n",
        "displacement, shift = space.periodic(box_size) \n",
        "\n",
        "kT = lambda t: np.where(t < 5000.0 * dt, 0.1, 0.01)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uFrxHj12M3X1"
      },
      "source": [
        "Next we need to generate some random positions as well as particle sizes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEic-SLigNIa"
      },
      "source": [
        "key = random.PRNGKey(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ApXCCgdWm9O"
      },
      "source": [
        "key, split = random.split(key)\n",
        "R = box_size * random.uniform(split, (N, dimension), dtype=np.float64)\n",
        "\n",
        "# The system ought to be a 50:50 mixture of two types of particles, one\n",
        "# large and one small.\n",
        "sigma = np.array([[1.0, 1.2], [1.2, 1.4]])\n",
        "N_2 = int(N / 2)\n",
        "species = np.where(np.arange(N) < N_2, 0, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hjPUEPcwM6jc"
      },
      "source": [
        "Then we need to construct our simulation operators."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B1xXV_FmWm9Q"
      },
      "source": [
        "energy_fn = energy.soft_sphere_pair(displacement, species=species, sigma=sigma)\n",
        "\n",
        "init, apply = simulate.nvt_nose_hoover(energy_fn, shift, dt, kT(0.))\n",
        "state = init(key, R)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJRvIrfiNAgV"
      },
      "source": [
        "Now let's actually do the simulation. To do this we'll write a small function that performs a single step of the simulation. This function will keep track of the temperature, the extended Hamiltonian of the Nose-Hoover dynamics, and the current particle positions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1rtYEp_LP35v"
      },
      "source": [
        "write_every = 100\n",
        "\n",
        "def step_fn(i, state_and_log):\n",
        "  state, log = state_and_log\n",
        "\n",
        "  t = i * dt\n",
        "\n",
        "  # Log information about the simulation.\n",
        "  T = quantity.temperature(state.velocity)\n",
        "  log['kT'] = ops.index_update(log['kT'], i, T)\n",
        "  H = simulate.nvt_nose_hoover_invariant(energy_fn, state, kT(t))\n",
        "  log['H'] = ops.index_update(log['H'], i, H)\n",
        "  # Record positions every `write_every` steps.\n",
        "  log['position'] = lax.cond(i % write_every == 0,\n",
        "                             lambda p: ops.index_update(p, \n",
        "                                                        i // write_every, \n",
        "                                                        state.position),\n",
        "                             lambda p: p,\n",
        "                             log['position'])\n",
        "\n",
        "  # Take a simulation step.\n",
        "  state = apply(state, kT=kT(t))\n",
        "  \n",
        "  return state, log"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "asUbxPn9lU6H"
      },
      "source": [
        "To run our simulation we'll use `lax.fori_loop` which will execute the simulation a single call from python."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "psqU1uEPWm9T"
      },
      "source": [
        "steps = 10000\n",
        "\n",
        "log = {\n",
        "    'kT': np.zeros((steps,)),\n",
        "    'H': np.zeros((steps,)),\n",
        "    'position': np.zeros((steps // write_every,) + R.shape) \n",
        "}\n",
        "\n",
        "state, log = lax.fori_loop(0, steps, step_fn, (state, log))\n",
        "\n",
        "R = state.position"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0hhEEuojNFht"
      },
      "source": [
        "Now, let's plot the temperature as a function of time. We see that the temperature tracks the goal temperature with some fluctuations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jiJOwCAPirFn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "69cb51fa-cb3a-4f0e-e521-1a8736b3b67f"
      },
      "source": [
        "t = onp.arange(0, steps) * dt\n",
        "plt.plot(t, log['kT'], linewidth=3)\n",
        "plt.plot(t, kT(t), linewidth=3)\n",
        "format_plot('$t$', '$T$')\n",
        "finalize_plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kTvH7BpVQtkm"
      },
      "source": [
        "Now let's plot the Hamiltonian of the system. We see that it is invariant apart from changes to the temperature, as expected."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDsowzLKQo3Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "d748438a-6d66-4bef-9c43-57cf5c7b41be"
      },
      "source": [
        "plt.plot(t, log['H'], linewidth=3)\n",
        "format_plot('$t$', '$H$')\n",
        "finalize_plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAGoCAYAAAATsnHAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATvElEQVR4nO3de/DsdX3f8dfC0SjEVD0RQg4xoJKPOjTipXiJsV4zah0xmYwDbQxxorEzxlibpl6mEzqd0tGOiZpOJw1GE5KJdzFaJ+OYEi9NUmmFMMIJeXsFI0VQChKUoOD2j90Tfh6PXJSzn/dhH4+Znd397p4fb75z9vc83+9+d7+L5XIZAOjmsNkDAMCBCBQALQkUAC0JFAAtCRQALe2aPcCd5dGPfvRyz549s8cA4A7au3fvl6vqfvsvv8sEas+ePTnnnHNmjwHAHTTGuOxAy+3iA6AlgQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAqClu8y3mX+v/uaL1+Xq678+ewz4NoctFnn4/e+de9zt8NmjwEYJVJLf/NNP5rfO/dTsMeA7+qEfuEc+/GtPFCm2il18ST7xhWtnjwC36ovX/X0u/Ft/T9kutqCSvPIZD8kP3ONu+fL1N84eBb7FxZd/Jdf9/U1Jkm9+czl5GtgsgUoyfuhe+a3THj57DPg2p531sfyvz149ewyYwi4+AFoSKABaEigAWhIoAFoSKABaEigAWhIoAFoSKABaEigAWhIoAFoSKABaEigAWhIoAFoSKABaEigAWhIoAFoSKABaEig4RDjhO9tGoABoSaCgscVi9gQwj0AB0JJAAdCSQAHQkkAB0JJAAdCSQAHQkkAB0JJAAdCSQAHQ0q7ZA4wxXprkhUkWSd5YVa8fY/z79bIvrZ/2qqr6k0kjAjDB1ECNMU7MKkQnJ/l6kg+MMd6/fvh1VfXaacMBMNXsLaiHJDmvqr6WJGOMjyT5mbkjAdDB7EBdnOTMMcbuJDckeWaSjye5OskvjzF+fn3/V6vqmnljArBpUw+SqKpLkrwmyQeTfCDJhUluTvLbSR6Y5KQkVyT5jVkzAjDH7C2oVNWbkrwpScYY/ynJF6rqyn2PjzHemOT93+GPA3AXNf0w8zHGUevr+2f1/tNbxhjH7HjKT2e1KxCALTJ9CyrJu9fvQX0jyYur6toxxn8ZY5yU1VmuL03yopkDQgdL53xny0wPVFX95AGWPW/GLAD0MX0XHwAciEBBY4vF7AlgHoECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBgkPEMsvZI8BGCRQALQkUNLaIc76zvQQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAoOEcvl7AlgswQKgJYEChpbLGZPAPMIFAAtCRQALQkUAC0JFAAt7Zo9wBjjpUlemGSR5I1V9foxxn2TvD3JcUkuTfLcqrpm2pAAbNzULagxxolZxenkJA9L8qwxxoOSvCLJuVV1QpJz1/cB2CKzd/E9JMl5VfW1qropyUeS/EySU5KcvX7O2UmeM2k+ACaZvYvv4iRnjjF2J7khyTOTfDzJ0VV1xfo5X0xy9KT5AJhk6hZUVV2S5DVJPpjkA0kuTHLzfs9ZJvElLwBbZvYuvlTVm6rqkVX1hCTXJPlkkivHGMckyfr6qpkzArB50wM1xjhqfX3/rN5/ekuS9yU5ff2U05O8d850AMwyPVBJ3j3G+Osk/z3Ji6vq2iSvTvK0Mcankjx1fR+ALTL7IIlU1U8eYNnVSZ4yYRwAmuiwBQUA30agAGhJoABoSaAAaEmg4BDh0+psG4ECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBgkPEcrmcPQJslEAB0JJAQWOLxWL2CDCNQAHQkkAB0JJAAdCSQAHQkkAB0JJAAdCSQAHQkkAB0JJAAdCSQAHQkkAB0JJAAdCSQAHQkkAB0JJAAdCSQAHQkkDBIcIJ39k2AgVASwIFjTnhO9ts1+wBxhgvS/KCrPZgXJTk+Un+W5J/muQr66f9QlVdOGdCAGaYGqgxxp4kv5LkoVV1wxjjHUlOXT/8a1X1rnnTATBTh118u5Lcc4yxK8kRSf7v5HkAaGBqoKrq8iSvTfL5JFck+UpVfXD98JljjE+MMV43xvi+aUMCMMXUQI0x7pPklCTHJ/nhJEeOMX4uySuTPDjJP0ly3yQvnzYkAFPM3sX31CSfq6ovVdU3kpyT5HFVdUVVLavqxiS/l+TkqVMCsHGzA/X5JI8ZYxwxxlgkeUqSS8YYxyTJetlzklw8cUYAJpj9HtR5Sd6V5IKsDjE/LMlZSf5ojHHRetkPJvmP04YEYIrpn4OqqjOSnLHf4ifPmAWAPmbv4gOAAxIoAFoSKABaEigAWhIoAFoSKABaEigAWhIoAFoSKDhULGcPAJslUAC0JFDQ2GIxewKYR6AAaEmgAGjpVr/NfIzxgiQfT3JRVd28mZEA4LZPt3FWVscO3TjG+ERWsdp32VtVjisC4KC4PYE6OcmJ6+uTc8vBrjeMMS7MjmhV1d8crEEB2C63Gqiq+pdJMsa4Z5JHJnlskhcmeVCSI5I8bn1Zrp93fZILqupJB3FmALbA7TqjblXdkOTPk/z5GOPHkzwwye4kj1hfHr6+PiHJEw7OqABsk+/6lO9VdW2SP1tfkiRjjCOTPOxOmAuALfddB+pAquqrSf7yzvyZAGwnn4MCoCWBAqClWw3UGOMJ6yP4AGCjbus9qA8nuWmMsTfJx5L87yRHHeyhAOC2AvU/sjp8/GHryy/te2CM8RdJLthx2VtVNx2kOQHYMrf1Qd2fSpIxxvFJHrXj8oisPrT72NzyzRJfX29pnV9VLzpoEwOwFW7vB3U/l+RzSd65b9kY44R8a7T2fVj34UkECoDvyffyQd1PJflUkrcmyRhjkeTBWcUKAL4nd9oHddffbH7J+gLcyZZx8gC2i89BAdCSQEFji9kDwEQCBUBLAgVASwIFQEsCBUBLAgVASwIFQEsCBUBLAgVASwIFQEsCBUBLAgVASwIFQEsCBUBLAgVAS3faCQu/W2OMlyV5QZJlkouSPD/JMUnelmR3kvOTPK+qvj5tSAA2buoW1BhjT5JfSfKoqjoxyeFJTk3ymiSvq6oHJbkmyS/OmxKAGTrs4tuV5J5jjF1JjkhyRZInJ3nX+vGzkzxn0mwATDI1UFV1eZLXJvl8VmH6Sla79K6tqpvWT/tCkj1zJoQ+lsvZE8Bmzd7Fd58kpyQ5PskPJzkyydNnzgSdLBZO+s72mr2L76lJPldVX6qqbyQ5J8lPJLn3epdfkhyb5PJZAwIwx+yj+D6f5DFjjCOS3JDkKUk+nuRDSX42qyP5Tk/y3mkTAjDF7PegzsvqYIgLsjrE/LAkZyV5eZJ/Pcb4dFaHmr9p2pAATDF7CypVdUaSM/Zb/NkkJ08YB4AmZr8HBQAHJFAAtCRQALQkUAC0JFAAtCRQALQkUAC0JFAAtCRQALQkUAC0JFAAtCRQALQkUAC0JFAAtCRQcIhYLmdPAJslUNDYYvYAMJFAAdCSQAHQkkAB0JJAAdCSQAHQkkAB0JJAAdCSQAHQkkAB0JJAAdCSQAHQkkAB0JJAAdCSQAHQkkAB0JJAAdCSQAHQkkDBIcIZ39k2AgVASwIFjS0WsyeAeQQKgJYECoCWBAqAlgQKgJYECoCWBAqAlgQKgJYECoCWBAqAlnbN/I+PMUaSt+9Y9IAkv57k3klemORL6+Wvqqo/2fB4AEw0NVBVVUlOSpIxxuFJLk/yniTPT/K6qnrtxPEAmKjTLr6nJPlMVV02exAA5usUqFOTvHXH/V8eY3xijPHmMcZ9Zg0FwBwtAjXGuHuSZyd553rRbyd5YFa7/65I8huTRgNgkqnvQe3wjCQXVNWVSbLvOknGGG9M8v5ZgwEwR4stqCSnZcfuvTHGMTse++kkF298IgCmmr4FNcY4MsnTkrxox+L/PMY4KauzXF+632MAbIHpgaqqrybZvd+y500aB9paLpezR4CN6rKLDwC+hUBBa4vZA8A0AgVASwIFQEsCBUBLAgVASwIFQEsCBUBLAgVASwIFQEsCBUBLAgVASwIFQEsCBUBLAgVASwIFQEsCBUBLAgVASwIFQEsCBYeI5ewBYMMEChpbOOM7W0ygAGhJoABoSaAAaEmgAGhJoABoSaAAaEmgAGhJoABoSaAAaEmgAGhJoABoSaAAaEmgAGhJoABoSaAAaEmgAGhJoABoSaAAaEmg4BCxXM6eADZLoKCxxewBYCKBAqAlgQKgJYECoCWBAqAlgQKgpV0z/+NjjJHk7TsWPSDJryf5g/Xy45JcmuS5VXXNpucDYJ6pW1C1clJVnZTkkUm+luQ9SV6R5NyqOiHJuev7AGyRTrv4npLkM1V1WZJTkpy9Xn52kudMmwqAKToF6tQkb13fPrqqrljf/mKSo+eMBMAsLQI1xrh7kmcneef+j1XVMokveQHYMi0CleQZSS6oqivX968cYxyTJOvrq6ZNBsAUXQJ1Wm7ZvZck70ty+vr26Uneu/GJAJhqeqDGGEcmeVqSc3YsfnWSp40xPpXkqev7AGyRqZ+DSpKq+mqS3fstuzqro/oA2FLTt6AA4EAECoCWBAqAlgQKgJYECg4ZPq/OdhEoaGyxmD0BzCNQALQkUAC0JFAAtCRQALQkUAC0JFAAtCRQALQkUAC0JFAAtCRQALQkUAC0JFAAtCRQALQkUAC0JFAAtCRQALS0a/YAwO3z7/54b/7i01f/w0kMF0kWO85ouFgkiyx23L797uiJERd34A/coR/tBI2HnMc8YHeeNI46KD9boOAQ8eXrb8wffuyy2WPAt/idj3w273/J43Pinn90p/9su/igsUcfv3v2CHCb7r7r4KTEFhQ09guPOy4POur7c9nVX80yyXK5Wr5cLrO+meUy68eW//Dndty8Tbf8pNv5/Dv0sw/Oz6WPxz5wd37s6HsdlJ8tUNDYYYct8oQfu1+S+80eBTbOLj4AWhIoAFoSKABaEigAWhIoAFoSKABaEigAWhIoAFoSKABaEigAWhIoAFoSKABaEigAWhIoAFq6y5xuY+/evV8eYzjdKMCh50cPtHCxdJYwABqyiw+AlgQKgJYECoCWBAqAlgQKgJYECoCW7jKfg/pejDGenuQNSQ5P8rtV9erJI23MGOPNSZ6V5KqqOnG97L5J3p7kuCSXJnluVV0za8ZNGWP8SJI/SHJ0kmWSs6rqDdu4PsYY90jy0STfl9XviXdV1RljjOOTvC3J7iTnJ3leVX193qSbMcY4PMnHk1xeVc/a4vVwaZK/S3Jzkpuq6lEH8/Wx9VtQ6794/zXJM5I8NMlpY4yHzp1qo34/ydP3W/aKJOdW1QlJzl3f3wY3JfnVqnpoksckefH678I2ro8bkzy5qh6W5KQkTx9jPCbJa5K8rqoelOSaJL84ccZNemmSS3bc39b1kCRPqqqTqupR6/sH7fWx9YFKcnKST1fVZ9f/AnpbklMmz7QxVfXRJP9vv8WnJDl7ffvsJM/Z6FCTVNUVVXXB+vbfZfULaU+2cH1U1bKqrl/fvdv6skzy5CTvWi/finUxxjg2yT9L8rvr+4ts4Xq4FQft9SFQq19Af7vj/hfWy7bZ0VV1xfr2F7Pa5bVVxhjHJXl4kvOypetjjHH4GOPCJFcl+dMkn0lybVXdtH7KtrxWXp/k3yb55vr+7mznekhW/0j54Bjj/DHGL62XHbTXh0Bxq6pqmdVfyq0xxvj+JO9O8q+q6rqdj23T+qiqm6vqpCTHZrWn4cGTR9q4Mca+92fPnz1LE4+vqkdk9ZbIi8cYT9j54J39+hCo5PIkP7Lj/rHrZdvsyjHGMUmyvr5q8jwbM8a4W1Zx+qOqOme9eGvXR5JU1bVJPpTksUnuPcbYd3DVNrxWfiLJs9cHB7wtq117b8j2rYckSVVdvr6+Ksl7svqHy0F7fQhU8n+SnDDGOH6McfckpyZ53+SZZntfktPXt09P8t6Js2zM+r2FNyW5pKp+c8dDW7c+xhj3G2Pce337nkmeltV7ch9K8rPrp93l10VVvbKqjq2q47L63fBnVfUvsmXrIUnGGEeOMe6173aSn0pycQ7i68O3mScZYzwzq/3Mhyd5c1WdOXmkjRljvDXJE5P8YJIrk5yR5I+TvCPJ/ZNcltVho/sfSHGXM8Z4fJL/meSi3PJ+w6uyeh9qq9bHGOPHs3rD+/Cs/iH7jqr6D2OMB2S1JXHfJH+V5Oeq6sZ5k27OGOOJSf7N+jDzrVsP6//n96zv7krylqo6c4yxOwfp9SFQALRkFx8ALQkUAC0JFAAtCRQALQkUAC0JFAAtCRQ0N8Z42RhjOcb457NngU0SKOjvketr3wfHVhEo6O+RSa5P8snZg8Am+SYJaGqM8eokL/8OD/98Vf3hJueBTXPKd+jrgqy+D+/0JH+Z1TmZ9vnwjIFgk2xBQWPrk8L9TpIXVdVZs+eBTfIeFPT2iPX1BVOngAkECnp7RJJvZHUKENgqAgVNrc/Y+o+T/PVd/VxDcCACBX09NMk9YvceW0qgoK+T1td/NXUKmESgoK/d6+vrpk4Bk/gcFPS176uNzhxjnJjkq0n2VtU7J84EG2MLCpqqqo8meUlWYXpJkjNyy24/uMvzQV0AWrIFBUBLAgVASwIFQEsCBUBLAgVASwIFQEsCBUBLAgVASwIFQEv/H/VnKA96q35TAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qtroRWfgqcT4"
      },
      "source": [
        "Now let's plot a snapshot of the system."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yq4Rz3eMqcTh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 873
        },
        "outputId": "b7cec16c-7941-4465-cc3f-ff8024de598c"
      },
      "source": [
        "ms = 65\n",
        "R_plt = onp.array(state.position)\n",
        "\n",
        "plt.plot(R_plt[:N_2, 0], R_plt[:N_2, 1], 'o', markersize=ms * 0.5)\n",
        "plt.plot(R_plt[N_2:, 0], R_plt[N_2:, 1], 'o', markersize=ms * 0.7)\n",
        "\n",
        "plt.xlim([0, np.max(R[:, 0])])\n",
        "plt.ylim([0, np.max(R[:, 1])])\n",
        "\n",
        "plt.axis('off')\n",
        "\n",
        "finalize_plot((2, 2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l4RLeAhVmA-_"
      },
      "source": [
        "If we want, we can also draw an animation of the simulation using JAX MD's renderer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "id": "79CiYF_aTgTq",
        "outputId": "11aafee1-9c6e-43b0-97e8-b2d205f119b2"
      },
      "source": [
        "from jax_md.colab_tools import renderer\n",
        "\n",
        "diameters = sigma[species, species]\n",
        "colors = np.where(species[:, None], \n",
        "                  np.array([[1.0, 0.5, 0.01]]), \n",
        "                  np.array([[0.35, 0.65, 0.85]]))\n",
        "\n",
        "renderer.render(box_size,\n",
        "                {\n",
        "                    'particles': renderer.Disk(log['position'], \n",
        "                                               diameters,\n",
        "                                               colors)\n",
        "                },\n",
        "                resolution=(700, 700))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<!--\n",
              "  Copyright 2020 Google LLC\n",
              "  Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "  you may not use this file except in compliance with the License.\n",
              "  You may obtain a copy of the License at\n",
              "      https://www.apache.org/licenses/LICENSE-2.0\n",
              "  Unless required by applicable law or agreed to in writing, software\n",
              "  distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "  See the License for the specific language governing permissions and\n",
              "  limitations under the License.\n",
              "-->\n",
              "\n",
              "<!--\n",
              "  A fragment of HTML and Javascript that describes a visualization tool.\n",
              "  \n",
              "  This code is expected to be injected into Jupyter or Colaboratory notebooks using the `IPython.display.HTML` function. The tool is rendered using WebGL2.\n",
              "-->\n",
              "\n",
              "<div id='seek'>\n",
              "  <button type='button'\n",
              "          id='pause_play' \n",
              "          style='width:40px; vertical-align:middle;' \n",
              "          onclick=\"toggle_play()\"> || \n",
              "  </button>\n",
              "  <input type=\"range\" \n",
              "         min=\"0\"\n",
              "         max=\"1\"\n",
              "         value=\"0\"\n",
              "         style=\"width:512px; vertical-align:middle;\"\n",
              "         class=\"slider\"\n",
              "         id=\"frame_range\"\n",
              "         oninput='change_frame(this.value)'>\n",
              "</div>\n",
              "<canvas id=\"canvas\"></canvas>\n",
              "<div id='info'> </div>\n",
              "<div id='error' style=\"color:red\"> </div>\n",
              "<script src=\"https://cdnjs.cloudflare.com/ajax/libs/gl-matrix/2.8.1/gl-matrix-min.js\"></script>\n",
              "\n",
              "<script>\n",
              "  var DIMENSION;\n",
              "\n",
              "  var SIZE;\n",
              "\n",
              "  var SHAPE = {};\n",
              "\n",
              "  var GEOMETRY = {};\n",
              "\n",
              "  var CURRENT_FRAME = 0;\n",
              "  var FRAME_COUNT = 0;\n",
              "\n",
              "  var BOX_SIZE;\n",
              "  var READ_BUFFER_SIZE = null;\n",
              "  var IS_LOADED = false;\n",
              "  var SIMULATION_IDX = 0;\n",
              "\n",
              "  // Info\n",
              "\n",
              "  var INFO = document.getElementById('info');\n",
              "  var ERROR = document.getElementById('error');\n",
              "\n",
              "  // Graphics\n",
              "\n",
              "  var GL;\n",
              "  var SHADER;\n",
              "  var BACKGROUND_COLOR = [0.2, 0.2, 0.2];\n",
              "\n",
              "  // 3D Camera\n",
              "\n",
              "  var EYE = mat4.create();\n",
              "  var PERSPECTIVE = mat4.create();\n",
              "  var LOOK_AT = mat4.create()\n",
              "  var YAW = 0.0;\n",
              "  var PITCH = 0.0;\n",
              "  var CAMERA_POSITION = mat4.create();\n",
              "  var Y_ROTATION_MATRIX = mat4.create();\n",
              "  var X_ROTATION_MATRIX = mat4.create();\n",
              "  var VIEW_DISTANCE = 0.0;\n",
              "\n",
              "  function make_look_at() {\n",
              "    var center = [BOX_SIZE[0] / 2.0, BOX_SIZE[1] / 2.0, BOX_SIZE[2] / 2.0];\n",
              "    var direction = [Math.cos(YAW) * Math.cos(PITCH),\n",
              "                     Math.sin(PITCH),\n",
              "                     Math.sin(YAW) * Math.cos(PITCH)];\n",
              "    var pos = [center[0] - VIEW_DISTANCE * direction[0],\n",
              "               center[1] - VIEW_DISTANCE * direction[1],\n",
              "               center[2] - VIEW_DISTANCE * direction[2]];\n",
              "    mat4.lookAt(LOOK_AT, \n",
              "                pos, \n",
              "                center, \n",
              "                [0.0, 1.0, 0.0]);\n",
              "  }\n",
              "\n",
              "  // 2D Camera\n",
              "\n",
              "  var SCREEN_POSITION = [0, 0];\n",
              "  var CAMERA_SIZE = [0, 0];\n",
              "\n",
              "  // Bonds\n",
              "\n",
              "  const BOND_SEGMENTS = 3;\n",
              "  const VERTICES_PER_BOND = BOND_SEGMENTS * 6;\n",
              "\n",
              "  // Simulation State\n",
              "\n",
              "  var IS_PLAYING = true;\n",
              "  var PLAY_PAUSE_BUTTON = document.getElementById('pause_play');\n",
              "\n",
              "  var FRAME_RANGE = document.getElementById('frame_range');\n",
              "\n",
              "  google.colab.output.setIframeHeight(0, true, {maxHeight: 5000});\n",
              "  var invokeFunction = google.colab.kernel.invokeFunction;\n",
              "\n",
              "  var CANVAS = document.getElementById(\"canvas\");\n",
              "  CANVAS.width = 1024;\n",
              "  CANVAS.height = 1024;\n",
              "\n",
              "  // Simulation Loading.\n",
              "\n",
              "  function make_sizes() {\n",
              "    return {\n",
              "      position: DIMENSION,\n",
              "      angle: DIMENSION - 1,\n",
              "      size: 1,\n",
              "      color: 3,\n",
              "    };\n",
              "  }\n",
              "\n",
              "  function simulation_info_string() {\n",
              "    return ('<p style=\"color:yellow\">' +\n",
              "            'Simulation Info:</p><div style=\"padding-left: 20px; padding-bottom: 10px;\">' +\n",
              "            'Box Size:    ' + BOX_SIZE.map(x => parseFloat(x).toFixed(2)) + '<br>' +\n",
              "            'Dimension:   ' + DIMENSION + '<br>' +\n",
              "            'Frame Count: ' + FRAME_COUNT + '<br></div>');\n",
              "  }\n",
              "\n",
              "  async function load_simulation() {\n",
              "    console.log(\"Loading simulation.\"); \n",
              "    INFO.innerHTML = 'Loading simulation...<br>';\n",
              "\n",
              "    var result = await invokeFunction('GetSimulationMetadata', [], {});\n",
              "    var metadata = from_json(result);\n",
              "\n",
              "    if(!metadata.box_size) {\n",
              "      ERROR.innerHTML += 'ERROR: No box size specified.<br>';\n",
              "    }\n",
              "\n",
              "    FRAME_COUNT = metadata.frame_count;\n",
              "    BOX_SIZE = metadata.box_size;\n",
              "    DIMENSION = metadata.dimension;\n",
              "    SIMULATION_IDX = metadata.simulation_idx;\n",
              "\n",
              "    if (metadata.background_color)\n",
              "      BACKGROUND_COLOR = metadata.background_color;\n",
              "\n",
              "    if (metadata.resolution) {\n",
              "      CANVAS.width = metadata.resolution[0];\n",
              "      CANVAS.height = metadata.resolution[1];\n",
              "    }\n",
              "\n",
              "    const aspect_ratio = CANVAS.width / CANVAS.height;\n",
              "\n",
              "    INFO.innerHTML += simulation_info_string();\n",
              "\n",
              "    SIZE = make_sizes();\n",
              "    initialize_gl();\n",
              "\n",
              "    if (DIMENSION == 2) {\n",
              "      SCREEN_POSITION = [BOX_SIZE[0] / 2.0, BOX_SIZE[1] / 2.0];\n",
              "      CAMERA_SIZE = [aspect_ratio * BOX_SIZE[0] / 2.0, BOX_SIZE[1] / 2.0];\n",
              "    } else if (DIMENSION == 3) {\n",
              "      const fovy = 45.0 / 180.0 * Math.PI;\n",
              "      const max_box_size = Math.max(BOX_SIZE[0], BOX_SIZE[1], BOX_SIZE[2]);\n",
              "      PERSPECTIVE = mat4.perspective(PERSPECTIVE, \n",
              "                                     fovy,            // Field of view.\n",
              "                                     aspect_ratio,    // Aspect ratio.\n",
              "                                     max_box_size / 10.0, // Near clip plane.\n",
              "                                     100 * max_box_size); // Far clip plane.\n",
              "      VIEW_DISTANCE = 2 * max_box_size;\n",
              "      make_look_at();\n",
              "    } else {\n",
              "      ERROR.innerHTML += 'ERROR: Invalid dimension specified: ' + DIMENSION + '.<br>';\n",
              "    }\n",
              "\n",
              "    FRAME_RANGE.max = FRAME_COUNT - 1;\n",
              "\n",
              "    // This specifies the maximum number of frames of data we will try to\n",
              "    // transfer between Python and Javascript at a time.\n",
              "    READ_BUFFER_SIZE = metadata.buffer_size;\n",
              "    if (!READ_BUFFER_SIZE)\n",
              "      READ_BUFFER_SIZE = FRAME_COUNT;\n",
              "\n",
              "    var geometry_list = metadata.geometry;\n",
              "    if (geometry_list) {\n",
              "      for (var i = 0; i < geometry_list.length ; i++) {\n",
              "        const name = geometry_list[i];\n",
              "        GEOMETRY[name] = await load_geometry(name);\n",
              "      }\n",
              "    }\n",
              "\n",
              "    IS_LOADED = true;\n",
              "  }\n",
              "\n",
              "  async function load_geometry(name) {\n",
              "    console.log('Loading ' + name + '.');\n",
              "    INFO.innerHTML += 'Loading geometry \"' + name + '\".<br>';\n",
              "    var result = await invokeFunction('GetGeometryMetadata' + SIMULATION_IDX,\n",
              "                                      [name], {});\n",
              "    var data = from_json(result);\n",
              "\n",
              "    console.log(data);\n",
              "\n",
              "    var geometry = {};\n",
              "    geometry.name = name;\n",
              "    geometry.shape = data.shape;\n",
              "    geometry.count = data.count;\n",
              "    geometry.selected = new Int32Array(data.count);\n",
              "\n",
              "    if (data.reference_geometry)\n",
              "      geometry.reference_geometry = data.reference_geometry;\n",
              "\n",
              "    if (data.max_neighbors)\n",
              "      geometry.max_neighbors = data.max_neighbors;\n",
              "\n",
              "    if(!geometry.shape) {\n",
              "      ERROR.innerHTML += 'ERROR: No shape specified for the geometry.<br>';\n",
              "    }\n",
              "\n",
              "    for (var field in data.fields) {\n",
              "      var array;\n",
              "      var array_type;\n",
              "      if (data.fields[field] == 'dynamic') {\n",
              "        array = await load_dynamic_array(name, field, geometry.count);\n",
              "        array_type = GL.DYNAMIC_DRAW;\n",
              "      } else if (data.fields[field] == 'static') {\n",
              "        array = await load_array(name, field);\n",
              "        array_type = GL.STATIC_DRAW;\n",
              "      } else if (data.fields[field] == 'global') {\n",
              "        array = await load_array(name, field);\n",
              "        array_type = 'GLOBAL';\n",
              "      } else {\n",
              "        ERROR.innerHTML += ('ERROR: field must have type \"dynamic\", \"static\", or ' +\n",
              "                            '\"global\". Found' + data.fields[field] + '.<br>');\n",
              "      }\n",
              "\n",
              "      geometry[field] = array;\n",
              "      geometry[field + '_type'] = array_type; \n",
              "\n",
              "      if (data.shape == 'Bond' && field == 'neighbor_idx')\n",
              "        continue;\n",
              "\n",
              "      if (array_type != 'GLOBAL') {\n",
              "        var array_buffer = GL.createBuffer();\n",
              "        var array_buffer_size = 4 * SIZE[field] * geometry.count;\n",
              "        GL.bindBuffer(GL.ARRAY_BUFFER, array_buffer);\n",
              "        GL.bufferData(GL.ARRAY_BUFFER, array, array_type);\n",
              "        geometry[field + '_buffer'] = array_buffer;  \n",
              "        geometry[field + '_buffer_size'] = array_buffer_size;   \n",
              "      }\n",
              "    }\n",
              "\n",
              "    if (data.shape == 'Bond') {\n",
              "      var vertex_buffer = GL.createBuffer();\n",
              "      var vertex_count = (geometry.count * \n",
              "                          geometry.max_neighbors * \n",
              "                          VERTICES_PER_BOND);\n",
              "      var vertex_buffer_size = 4 * SIZE['position'] * vertex_count;\n",
              "      GL.bindBuffer(GL.ARRAY_BUFFER, vertex_buffer);\n",
              "      GL.bufferData(GL.ARRAY_BUFFER, vertex_buffer_size, GL.DYNAMIC_DRAW);\n",
              "\n",
              "      geometry.vertices = new Float32Array(SIZE['position'] * vertex_count);\n",
              "      geometry.vertex_buffer = vertex_buffer;\n",
              "      geometry.vertex_buffer_size = vertex_buffer_size;\n",
              "\n",
              "      var vertex_normal_buffer = GL.createBuffer();\n",
              "      GL.bindBuffer(GL.ARRAY_BUFFER, vertex_normal_buffer);\n",
              "      GL.bufferData(GL.ARRAY_BUFFER, vertex_buffer_size, GL.DYNAMIC_DRAW);\n",
              "\n",
              "      geometry.normals = new Float32Array(SIZE['position'] * vertex_count);\n",
              "      geometry.vertex_normal_buffer = vertex_normal_buffer;\n",
              "    }\n",
              "\n",
              "    return geometry;\n",
              "  }\n",
              "\n",
              "  async function load_dynamic_array(name, field, count) {\n",
              "    if (!FRAME_COUNT) {\n",
              "      ERROR.innerHTML += 'ERROR: No frame count specified.<br>';\n",
              "    }\n",
              "\n",
              "    var array = new Float32Array(FRAME_COUNT * count * SIZE[field]);\n",
              "\n",
              "    const info_text = INFO.innerHTML;\n",
              "\n",
              "    for (var i = 0 ; i < FRAME_COUNT ; i += READ_BUFFER_SIZE) {\n",
              "      await load_array_chunk(name, field, count, array, i, info_text);\n",
              "    }\n",
              "\n",
              "    INFO.innerHTML = info_text + 'Loaded \"' + field + '\".<br>';\n",
              "\n",
              "    return array;\n",
              "  }\n",
              "\n",
              "  async function load_array_chunk(name, field, count, array, offset, info_text) {\n",
              "    var dbg_string = ('Loading \"' + field + '\" chunk at time offset ' + offset +\n",
              "                      '.<br>'); \n",
              "    console.log(dbg_string);\n",
              "    INFO.innerHTML = info_text + dbg_string + '<br>';\n",
              "\n",
              "    var result = await invokeFunction('GetArrayChunk' + SIMULATION_IDX, \n",
              "                                      [name, field, offset, READ_BUFFER_SIZE],\n",
              "                                      {});\n",
              "    var data = from_json(result);\n",
              "\n",
              "    if (!data.array_chunk) {\n",
              "      // Error checking.\n",
              "    }\n",
              "\n",
              "    var array_chunk = decode(data.array_chunk);\n",
              "    array.set(array_chunk, offset * count * SIZE[field]);\n",
              "  }\n",
              "\n",
              "  async function load_array(name, field) {\n",
              "    const info_text = INFO.innerHTML;\n",
              "    INFO.innerHTML += 'Loading \"' + field + '\".<br>';\n",
              "    var result = await invokeFunction('GetArray' + SIMULATION_IDX,\n",
              "                                      [name, field], {});\n",
              "    var data = from_json(result);\n",
              "\n",
              "    if (!data.array) {\n",
              "      ERROR.innerHTML += 'ERROR: No data array specified.<br>';\n",
              "    }\n",
              "    INFO.innerHTML = info_text + 'Loaded \"' + field + '\".<br>';\n",
              "    return decode(data.array);\n",
              "  }\n",
              "\n",
              "  function initialize_gl() {\n",
              "    GL = CANVAS.getContext(\"webgl2\");\n",
              "\n",
              "    if (!GL) {\n",
              "        alert('Unable to initialize WebGL.');\n",
              "        return;\n",
              "    }\n",
              "\n",
              "    GL.viewport(0, 0, GL.drawingBufferWidth, GL.drawingBufferHeight);\n",
              "\n",
              "    if (BACKGROUND_COLOR)\n",
              "      GL.clearColor(BACKGROUND_COLOR[0], \n",
              "                    BACKGROUND_COLOR[1], \n",
              "                    BACKGROUND_COLOR[2], \n",
              "                    1.0);\n",
              "    else\n",
              "      GL.clearColor(0.2, 0.2, 0.2, 1.0);\n",
              "    GL.enable(GL.DEPTH_TEST);\n",
              "\n",
              "    var shader_program;\n",
              "    if (DIMENSION == 2)\n",
              "      shader_program = initialize_shader(\n",
              "          GL, VERTEX_SHADER_SOURCE_2D, FRAGMENT_SHADER_SOURCE_2D);\n",
              "    else if (DIMENSION == 3)\n",
              "      shader_program = initialize_shader(\n",
              "          GL, VERTEX_SHADER_SOURCE_3D, FRAGMENT_SHADER_SOURCE_3D);\n",
              "\n",
              "    SHADER = {\n",
              "      program: shader_program,\n",
              "      attribute: {\n",
              "          vertex_position: GL.getAttribLocation(shader_program, 'vertex_position'),\n",
              "          position: GL.getAttribLocation(shader_program, 'position'),\n",
              "          size: GL.getAttribLocation(shader_program, 'size'),\n",
              "          color: GL.getAttribLocation(shader_program, 'color'),\n",
              "      },\n",
              "      uniform: {\n",
              "          color: GL.getUniformLocation(shader_program, 'color'),\n",
              "          global_size: GL.getUniformLocation(shader_program, 'global_size'),\n",
              "          use_global_size: GL.getUniformLocation(shader_program, 'use_global_size'),\n",
              "          global_color: GL.getUniformLocation(shader_program, 'global_color'),\n",
              "          use_global_color: GL.getUniformLocation(shader_program, 'use_global_color'),\n",
              "          use_position: GL.getUniformLocation(shader_program, 'use_position')\n",
              "      },\n",
              "    };\n",
              "\n",
              "    if (DIMENSION == 2) {\n",
              "      SHADER.uniform['screen_position'] = GL.getUniformLocation(shader_program, 'screen_position');\n",
              "      SHADER.uniform['screen_size'] = GL.getUniformLocation(shader_program, 'screen_size');\n",
              "    } else if (DIMENSION == 3) {\n",
              "      SHADER.attribute['vertex_normal'] = GL.getAttribLocation(shader_program, 'vertex_normal');\n",
              "      SHADER.uniform['perspective'] = GL.getUniformLocation(shader_program, 'perspective');\n",
              "      SHADER.uniform['light_direction'] = GL.getUniformLocation(shader_program, 'light_direction');\n",
              "    }\n",
              "\n",
              "    GL.useProgram(shader_program);\n",
              "    \n",
              "    GL.uniform4f(SHADER.uniform.color, 0.9, 0.9, 1.0, 1.0);\n",
              "    GL.uniform1f(SHADER.uniform.global_size, 1.0);\n",
              "    GL.uniform1i(SHADER.uniform.use_global_size, true);\n",
              "\n",
              "    GL.uniform3f(SHADER.uniform.global_color, 1.0, 1.0, 1.0);\n",
              "    GL.uniform1i(SHADER.uniform.use_global_color, true);\n",
              "    GL.uniform1f(SHADER.uniform.use_position, 1.0);\n",
              "\n",
              "    var inorm = 1.0 / Math.sqrt(3);\n",
              "    GL.uniform3f(SHADER.uniform.light_direction, inorm, -inorm, inorm)\n",
              "\n",
              "    SHAPE['Disk'] = make_disk(GL, SHADER, 16, 0.5);\n",
              "    SHAPE['Sphere'] = make_sphere(GL, SHADER, 16, 16, 0.5);\n",
              "\n",
              "    const vao = GL.createVertexArray();\n",
              "    GL.bindVertexArray(vao);\n",
              "  }\n",
              "\n",
              "  function make_disk(gl, shader, segments, radius) {\n",
              "    var position = new Float32Array(segments * DIMENSION * 3);\n",
              "    for (var s = 0 ; s < segments ; s++) {\n",
              "        const th = 2 * s / segments * Math.PI;\n",
              "        const th_p = 2 * (s + 1) / segments * Math.PI;\n",
              "        position.set([0.0, 0.0], s * 3 * DIMENSION);\n",
              "        position.set([radius * Math.cos(th), radius * Math.sin(th)],\n",
              "                     s * 3 * DIMENSION + DIMENSION);\n",
              "        position.set([radius * Math.cos(th_p), radius * Math.sin(th_p)], \n",
              "                     s * 3 * DIMENSION + 2 * DIMENSION);\n",
              "    }\n",
              "\n",
              "    var buffer = gl.createBuffer();\n",
              "    gl.bindBuffer(gl.ARRAY_BUFFER, buffer);\n",
              "    gl.bufferData(gl.ARRAY_BUFFER, position, gl.STATIC_DRAW);\n",
              "\n",
              "    return {\n",
              "      vertex_position: position,\n",
              "      vertex_buffer: buffer,\n",
              "      vertex_count: segments * 3,\n",
              "    };\n",
              "  }\n",
              "\n",
              "  function make_sphere(gl, shader, horizontal_segments, vertical_segments, radius) {\n",
              "    const stride = DIMENSION * 3 * 2;  // 3 vertices per tri, two tris per quad.\n",
              "    if (DIMENSION != 3) {\n",
              "      return null;\n",
              "      // Error Checking.\n",
              "    }\n",
              "\n",
              "    var position = new Float32Array(vertical_segments * horizontal_segments * stride);\n",
              "    var normal = new Float32Array(vertical_segments * horizontal_segments * stride);\n",
              "\n",
              "    var dtheta = 2 * Math.PI / horizontal_segments;\n",
              "    var dphi = Math.PI / vertical_segments;\n",
              "\n",
              "    for (var vs = 0 ; vs < vertical_segments ; vs++) {\n",
              "      const phi_0 = vs * dphi;\n",
              "      const phi_1 = (vs + 1) * dphi;\n",
              "      const offset = vs * horizontal_segments * stride;\n",
              "      for (var hs = 0 ; hs < horizontal_segments ; hs++) {\n",
              "        const theta_0 = hs * dtheta;\n",
              "        const theta_1 = (hs + 1) * dtheta;\n",
              "\n",
              "        const x0 = radius * Math.sin(phi_0) * Math.cos(theta_0);\n",
              "        const y0 = radius * Math.sin(phi_0) * Math.sin(theta_0);\n",
              "        const z0 = radius * Math.cos(phi_0);\n",
              "\n",
              "        const x1 = radius * Math.sin(phi_1) * Math.cos(theta_0);\n",
              "        const y1 = radius * Math.sin(phi_1) * Math.sin(theta_0);\n",
              "        const z1 = radius * Math.cos(phi_1);\n",
              "\n",
              "        const x2 = radius * Math.sin(phi_0) * Math.cos(theta_1);\n",
              "        const y2 = radius * Math.sin(phi_0) * Math.sin(theta_1);\n",
              "        const z2 = radius * Math.cos(phi_0);\n",
              "\n",
              "        const x3 = radius * Math.sin(phi_1) * Math.cos(theta_1);\n",
              "        const y3 = radius * Math.sin(phi_1) * Math.sin(theta_1);\n",
              "        const z3 = radius * Math.cos(phi_1);\n",
              "\n",
              "        position.set([x0, y0, z0,\n",
              "                      x1, y1, z1,\n",
              "                      x2, y2, z2,\n",
              "                      x1, y1, z1,\n",
              "                      x3, y3, z3,\n",
              "                      x2, y2, z2], offset + hs * stride);\n",
              "\n",
              "        normal.set([x0 / radius, y0 / radius, z0 / radius,\n",
              "                    x1 / radius, y1 / radius, z1 / radius,\n",
              "                    x2 / radius, y2 / radius, z2 / radius,\n",
              "                    x1 / radius, y1 / radius, z1 / radius,\n",
              "                    x3 / radius, y3 / radius, z3 / radius,\n",
              "                    x2 / radius, y2 / radius, z2 / radius], offset + hs * stride);              \n",
              "      }\n",
              "    }\n",
              "\n",
              "    var buffer = gl.createBuffer();\n",
              "    gl.bindBuffer(gl.ARRAY_BUFFER, buffer);\n",
              "    gl.bufferData(gl.ARRAY_BUFFER, position, gl.STATIC_DRAW);\n",
              "\n",
              "    var normal_buffer = gl.createBuffer();\n",
              "    gl.bindBuffer(gl.ARRAY_BUFFER, normal_buffer);\n",
              "    gl.bufferData(gl.ARRAY_BUFFER, normal, gl.STATIC_DRAW);\n",
              "\n",
              "    return {\n",
              "      vertex_position: position,\n",
              "      vertex_buffer: buffer,\n",
              "      vertex_normals: normal,\n",
              "      vertex_normal_buffer: normal_buffer,\n",
              "      vertex_count: vertical_segments * horizontal_segments * 3 * 2\n",
              "    };\n",
              "  }\n",
              "\n",
              "  function set_attribute(geometry, name) {\n",
              "    if (!geometry[name]) {\n",
              "      if (SIZE[name] == 1)\n",
              "        GL.uniform1f(SHADER.uniform['global_' + name], 1.0);\n",
              "      else if (SIZE[name] == 2)\n",
              "        GL.uniform2f(SHADER.uniform['global_' + name], 1.0, 1.0);\n",
              "      else if (SIZE[name] == 3)\n",
              "        GL.uniform3f(SHADER.uniform['global_' + name], 1.0, 1.0, 1.0);\n",
              "\n",
              "      GL.uniform1i(SHADER.uniform['use_global_' + name], true);\n",
              "    } else if (geometry[name + '_type'] == 'GLOBAL') {\n",
              "      if (SIZE[name] == 1)\n",
              "        GL.uniform1fv(SHADER.uniform['global_' + name], geometry[name]);\n",
              "      else if (SIZE[name] == 2)\n",
              "        GL.uniform2fv(SHADER.uniform['global_' + name], geometry[name]);\n",
              "      else if (SIZE[name] == 3)\n",
              "        GL.uniform3fv(SHADER.uniform['global_' + name], geometry[name]);\n",
              "\n",
              "      GL.uniform1i(SHADER.uniform['use_global_' + name], true);\n",
              "    } else {\n",
              "      GL.enableVertexAttribArray(SHADER.attribute[name]);\n",
              "      var stride = SIZE[name] * geometry.count;\n",
              "      GL.bindBuffer(GL.ARRAY_BUFFER, geometry[name + '_buffer']);\n",
              "      if (geometry[name + '_type'] == GL.DYNAMIC_DRAW) {\n",
              "        const data = geometry[name].slice(CURRENT_FRAME * stride, \n",
              "                                          (CURRENT_FRAME + 1) * stride);\n",
              "        GL.bufferSubData(GL.ARRAY_BUFFER, 0, data);\n",
              "      }\n",
              "      GL.vertexAttribPointer(\n",
              "        SHADER.attribute[name],               \n",
              "        SIZE[name],        \n",
              "        GL.FLOAT,         \n",
              "        false,            \n",
              "        0,    \n",
              "        0\n",
              "      );                \n",
              "      GL.vertexAttribDivisor(SHADER.attribute[name], 1);\n",
              "\n",
              "      GL.uniform1i(SHADER.uniform['use_global_' + name], false);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  var loops = 0;\n",
              "\n",
              "  function update_frame() {\n",
              "    if(!GL) {\n",
              "      window.requestAnimationFrame(update_frame);\n",
              "      return;\n",
              "    }\n",
              "\n",
              "    GL.clear(GL.COLOR_BUFFER_BIT | GL.DEPTH_BUFFER_BIT);\n",
              "    \n",
              "    if (!IS_LOADED) {\n",
              "      window.requestAnimationFrame(update_frame);\n",
              "      return;\n",
              "    }\n",
              "\n",
              "    if (DIMENSION == 2) {\n",
              "      var camera_x = SCREEN_POSITION[0];\n",
              "      var camera_y = SCREEN_POSITION[1];\n",
              "\n",
              "      if (DRAGGING) {\n",
              "        const delta = get_drag_offset();\n",
              "        camera_x += delta[0];\n",
              "        camera_y += delta[1];\n",
              "      }\n",
              "      GL.uniform2f(SHADER.uniform.screen_position, camera_x, camera_y);\n",
              "      GL.uniform2f(SHADER.uniform.screen_size, CAMERA_SIZE[0], CAMERA_SIZE[1]);\n",
              "    } else if (DIMENSION == 3) {\n",
              "\n",
              "      // Now these are some janky globals.\n",
              "      if (DRAGGING) {\n",
              "        var yaw = YAW;\n",
              "        var pitch = PITCH;\n",
              "        const delta = get_drag_offset();\n",
              "        YAW = YAW - delta[0];\n",
              "        PITCH = PITCH - delta[1];\n",
              "        make_look_at();\n",
              "        YAW = yaw;\n",
              "        PITCH = pitch;\n",
              "      }\n",
              "\n",
              "      GL.uniformMatrix4fv(SHADER.uniform.perspective, false,\n",
              "                          mat4.multiply(EYE, PERSPECTIVE, LOOK_AT));\n",
              "    }\n",
              "\n",
              "    if (CURRENT_FRAME > FRAME_COUNT - 1) {\n",
              "      loops++;\n",
              "      CURRENT_FRAME = 0;\n",
              "    }\n",
              "\n",
              "    for (var name in GEOMETRY) {\n",
              "      var geom = GEOMETRY[name];\n",
              "\n",
              "      set_attribute(geom, 'size');\n",
              "      set_attribute(geom, 'color');\n",
              "\n",
              "      var shape = geom.shape;\n",
              "      var vertex_buffer;\n",
              "      var vertex_count;\n",
              "      var vertex_normal_buffer = null;\n",
              "\n",
              "      if (shape != 'Bond') {\n",
              "        shape = SHAPE[shape];\n",
              "\n",
              "        update_shape(geom);        \n",
              "\n",
              "        vertex_buffer = shape.vertex_buffer;\n",
              "        vertex_count = shape.vertex_count;\n",
              "        vertex_normal_buffer = shape.vertex_normal_buffer;\n",
              "      } else {\n",
              "        vertex_count = update_bond_vertex_data(GEOMETRY[geom.reference_geometry],\n",
              "                                               geom);\n",
              "        vertex_buffer = geom.vertex_buffer;\n",
              "        vertex_normal_buffer = geom.vertex_normal_buffer;\n",
              "      }\n",
              "\n",
              "      GL.enableVertexAttribArray(SHADER.attribute.vertex_position);\n",
              "      GL.bindBuffer(GL.ARRAY_BUFFER, vertex_buffer);\n",
              "      GL.vertexAttribPointer(\n",
              "        SHADER.attribute.vertex_position,\n",
              "        DIMENSION,\n",
              "        GL.FLOAT,\n",
              "        false,\n",
              "        0,\n",
              "        0\n",
              "      );\n",
              "\n",
              "      if (DIMENSION == 3 && vertex_normal_buffer) { \n",
              "        GL.enableVertexAttribArray(SHADER.attribute.vertex_normal);\n",
              "        GL.bindBuffer(GL.ARRAY_BUFFER, vertex_normal_buffer);\n",
              "        GL.vertexAttribPointer(\n",
              "          SHADER.attribute.vertex_normal,\n",
              "          DIMENSION,\n",
              "          GL.FLOAT,\n",
              "          false,\n",
              "          0,\n",
              "          0\n",
              "        );\n",
              "      }\n",
              "\n",
              "      if (shape == 'Bond')\n",
              "      {\n",
              "        GL.drawArrays(GL.TRIANGLES, 0, vertex_count);\n",
              "      }\n",
              "      else {\n",
              "        GL.drawArraysInstanced(GL.TRIANGLES, 0, vertex_count, geom.count);\n",
              "      }\n",
              "    }\n",
              "\n",
              "    if(IS_PLAYING) {\n",
              "      CURRENT_FRAME++;\n",
              "      FRAME_RANGE.value = CURRENT_FRAME;\n",
              "    }\n",
              "\n",
              "    window.requestAnimationFrame(update_frame);\n",
              "  }\n",
              "\n",
              "  function update_shape(geometry) {\n",
              "    GL.enableVertexAttribArray(SHADER.attribute.position);\n",
              "    var stride = geometry.count * DIMENSION;\n",
              "    GL.bindBuffer(GL.ARRAY_BUFFER, geometry.position_buffer);\n",
              "    if (geometry.position_type == GL.DYNAMIC_DRAW) {\n",
              "      const positions = geometry.position.subarray(CURRENT_FRAME * stride, \n",
              "                                                   (CURRENT_FRAME + 1) * stride);\n",
              "      GL.bufferSubData(GL.ARRAY_BUFFER, 0, positions);\n",
              "    }\n",
              "    GL.vertexAttribPointer(\n",
              "      SHADER.attribute.position,               \n",
              "      DIMENSION,        \n",
              "      GL.FLOAT,         \n",
              "      false,            \n",
              "      0,    \n",
              "      0\n",
              "    );                \n",
              "    GL.vertexAttribDivisor(SHADER.attribute.position, 1);\n",
              "    GL.uniform1f(SHADER.uniform.use_position, 1.0);\n",
              "  }\n",
              "\n",
              "  function update_bond_vertex_data(reference_geometry, bonds) {\n",
              "    const geom = reference_geometry;\n",
              "    const N = geom.count;\n",
              "    var stride = N * DIMENSION;\n",
              "    const positions = geom.position.subarray(CURRENT_FRAME * stride, \n",
              "                                             (CURRENT_FRAME + 1) * stride);\n",
              "    const neighbors = bonds.max_neighbors;\n",
              "\n",
              "    var vertex_count = 0;\n",
              "    var vertices = bonds.vertices;\n",
              "    var normals = bonds.normals;\n",
              "\n",
              "    for (var i = 0 ; i < N ; i++) {\n",
              "      var r_i = positions.subarray(i * DIMENSION, (i + 1) * DIMENSION);\n",
              "      for (var j = 0 ; j < neighbors ; j++) {\n",
              "        const idx = i * neighbors + j; \n",
              "        const nbr_idx = Math.round(bonds.neighbor_idx[idx]);\n",
              "\n",
              "        if (nbr_idx < i) {\n",
              "          var r_j = positions.subarray(nbr_idx * DIMENSION, (nbr_idx + 1) * DIMENSION);\n",
              "          vertex_count = push_bond(vertices, normals, vertex_count, r_i, r_j, 0.1); //bonds.diameter / 2.0);\n",
              "        }\n",
              "      }\n",
              "    }\n",
              "\n",
              "    GL.bindBuffer(GL.ARRAY_BUFFER, bonds.vertex_buffer);\n",
              "    GL.bufferData(GL.ARRAY_BUFFER, vertices, GL.DYNAMIC_DRAW);\n",
              "\n",
              "    GL.bindBuffer(GL.ARRAY_BUFFER, bonds.vertex_normal_buffer);\n",
              "    GL.bufferData(GL.ARRAY_BUFFER, normals, GL.DYNAMIC_DRAW);\n",
              "\n",
              "    GL.uniform1f(SHADER.uniform.use_position, 0.0);\n",
              "    GL.uniform1i(SHADER.uniform.use_global_size, 1);\n",
              "    GL.uniform1i(SHADER.uniform.use_global_color, 1);\n",
              "\n",
              "    return vertex_count;\n",
              "  }\n",
              "\n",
              "  BOND_C_TABLE = [];\n",
              "  BOND_S_TABLE = [];\n",
              "  for (var i = 0 ; i < BOND_SEGMENTS ; i++)\n",
              "  {\n",
              "    BOND_C_TABLE.push(Math.cos(2 * i * Math.PI / BOND_SEGMENTS));\n",
              "    BOND_S_TABLE.push(Math.sin(2 * i * Math.PI / BOND_SEGMENTS));\n",
              "  }\n",
              "\n",
              "  function push_bond(vertices, normals, vertex_count, r_i, r_j, radius) {\n",
              "    var dr = vec3.fromValues(r_i[0] - r_j[0], \n",
              "                             r_i[1] - r_j[1], \n",
              "                             r_i[2] - r_j[2]);\n",
              "\n",
              "    if (Math.abs(dr[0]) > BOX_SIZE[0] / 2.0 || \n",
              "        Math.abs(dr[1]) > BOX_SIZE[1] / 2.0 ||\n",
              "        Math.abs(dr[2]) > BOX_SIZE[2] / 2.0)\n",
              "      return vertex_count;\n",
              "\n",
              "    var up = vec3.fromValues(0.0, 1.0, 0.0);\n",
              "    var left = vec3.fromValues(0.0, 1.0, 0.0);\n",
              "\n",
              "    vec3.cross(left, up, dr);\n",
              "    vec3.normalize(left, left);\n",
              "\n",
              "    vec3.cross(up, left, dr);\n",
              "    vec3.normalize(up, up);\n",
              "\n",
              "    var normal = vec3.fromValues(0.0, 0.0, 0.0);\n",
              " \n",
              "    for (var i = 0 ; i < BOND_SEGMENTS ; i++) {\n",
              "      const c1 = radius * BOND_C_TABLE[i];\n",
              "      const c2 = radius * BOND_C_TABLE[(i + 1) % BOND_SEGMENTS];\n",
              "      const s1 = radius * BOND_S_TABLE[i];\n",
              "      const s2 = radius * BOND_S_TABLE[(i + 1) % BOND_SEGMENTS];\n",
              "\n",
              "      vertices.set([\n",
              "        r_j[0] + left[0] * c1 + up[0] * s1,\n",
              "        r_j[1] + left[1] * c1 + up[1] * s1,\n",
              "        r_j[2] + left[2] * c1 + up[2] * s1,\n",
              "\n",
              "        r_i[0] + left[0] * c1 + up[0] * s1,\n",
              "        r_i[1] + left[1] * c1 + up[1] * s1,\n",
              "        r_i[2] + left[2] * c1 + up[2] * s1,\n",
              "\n",
              "        r_j[0] + left[0] * c2 + up[0] * s2,\n",
              "        r_j[1] + left[1] * c2 + up[1] * s2,\n",
              "        r_j[2] + left[2] * c2 + up[2] * s2,\n",
              "\n",
              "        r_i[0] + left[0] * c1 + up[0] * s1,\n",
              "        r_i[1] + left[1] * c1 + up[1] * s1,\n",
              "        r_i[2] + left[2] * c1 + up[2] * s1,\n",
              "\n",
              "        r_i[0] + left[0] * c2 + up[0] * s2,\n",
              "        r_i[1] + left[1] * c2 + up[1] * s2,\n",
              "        r_i[2] + left[2] * c2 + up[2] * s2,\n",
              "\n",
              "        r_j[0] + left[0] * c2 + up[0] * s2,\n",
              "        r_j[1] + left[1] * c2 + up[1] * s2,\n",
              "        r_j[2] + left[2] * c2 + up[2] * s2,        \n",
              "      ], 3 * (vertex_count + 6 * i));\n",
              "\n",
              "      vec3.cross(normal, \n",
              "                 [r_j[0] - r_i[0] + left[0] * c1 + up[0] * s1, \n",
              "                  r_j[1] - r_i[1] + left[1] * c1 + up[1] * s1, \n",
              "                  r_j[2] - r_i[2] + left[2] * c1 + up[2] * s1],\n",
              "                 [left[0] * (c1 - c2) + up[0] * (s1 - s2), \n",
              "                  left[1] * (c1 - c2) + up[1] * (s1 - s2), \n",
              "                  left[2] * (c1 - c2) + up[2] * (s1 - s2)]);\n",
              "      vec3.normalize(normal, normal);\n",
              "\n",
              "      normals.set([\n",
              "        normal[0], normal[1], normal[2],\n",
              "        normal[0], normal[1], normal[2],\n",
              "        normal[0], normal[1], normal[2],\n",
              "        normal[0], normal[1], normal[2],\n",
              "        normal[0], normal[1], normal[2],\n",
              "        normal[0], normal[1], normal[2],\n",
              "      ], 3 * (vertex_count + 6 * i));\n",
              "    }\n",
              "    return vertex_count + 6 * BOND_SEGMENTS;\n",
              "  }\n",
              "\n",
              "  // SHADER CODE\n",
              "\n",
              "  const VERTEX_SHADER_SOURCE_2D = `#version 300 es\n",
              "    // Vertex Shader Program.\n",
              "    in vec2 vertex_position;\n",
              "    in vec2 position;\n",
              "    in float size;\n",
              "    in vec3 color;\n",
              "\n",
              "    out vec4 v_color;\n",
              "\n",
              "    uniform float use_position;\n",
              "\n",
              "    uniform vec2 screen_position;\n",
              "    uniform vec2 screen_size;\n",
              "\n",
              "    uniform float global_size;\n",
              "    uniform bool use_global_size;\n",
              "\n",
              "    uniform vec3 global_color;\n",
              "    uniform bool use_global_color;\n",
              "\n",
              "    void main() {\n",
              "      float _size = use_global_size ? global_size : size;\n",
              "      vec2 v = (_size * vertex_position + position - screen_position) / screen_size;\n",
              "      gl_Position = vec4(v, 0.0, 1.0);\n",
              "      v_color = vec4(use_global_color ? global_color : color, 1.0f);\n",
              "    }\n",
              "  `;\n",
              "\n",
              "  const FRAGMENT_SHADER_SOURCE_2D = `#version 300 es\n",
              "    precision mediump float;\n",
              "\n",
              "    in vec4 v_color;\n",
              "\n",
              "    out vec4 outColor;\n",
              "\n",
              "    void main() {\n",
              "      outColor = v_color;\n",
              "    }\n",
              "  `;\n",
              "\n",
              "   const VERTEX_SHADER_SOURCE_3D = `#version 300 es\n",
              "    // Vertex Shader Program.\n",
              "    in vec3 vertex_position;\n",
              "    in vec3 vertex_normal;\n",
              "\n",
              "    in vec3 position;\n",
              "    in float size;\n",
              "    in vec3 color;\n",
              "\n",
              "    out vec4 v_color;\n",
              "    out vec3 v_normal;\n",
              "\n",
              "    uniform mat4 perspective;\n",
              "\n",
              "    uniform float use_position;\n",
              "\n",
              "    uniform float global_size;\n",
              "    uniform bool use_global_size;\n",
              "\n",
              "    uniform vec3 global_color;\n",
              "    uniform bool use_global_color;\n",
              "\n",
              "    void main() {\n",
              "      vec3 pos = use_position * position;\n",
              "      float _size = use_global_size ? global_size : size;\n",
              "\n",
              "      vec3 v = (_size * vertex_position + pos);\n",
              "      gl_Position = perspective * vec4(v, 1.0);\n",
              "\n",
              "      v_color = vec4(use_global_color ? global_color : color, 1.0f);\n",
              "      v_normal = vertex_normal;\n",
              "    }\n",
              "  `;\n",
              "\n",
              "  const FRAGMENT_SHADER_SOURCE_3D = `#version 300 es\n",
              "    precision mediump float;\n",
              "\n",
              "    in vec4 v_color;\n",
              "    in vec3 v_normal;\n",
              "\n",
              "    uniform vec3 light_direction;\n",
              "\n",
              "    out vec4 outColor;\n",
              "\n",
              "    void main() {\n",
              "      float diffuse_magnitude = clamp(-dot(v_normal, light_direction), 0.0, 1.0) + 0.2;\n",
              "\n",
              "      outColor = vec4(vec3(v_color) * diffuse_magnitude, v_color.a);\n",
              "    }\n",
              "  `;\n",
              "\n",
              "  function initialize_shader(gl, vertex_shader_source, fragment_shader_source) {\n",
              "\n",
              "    const vertex_shader = compile_shader(\n",
              "      gl, gl.VERTEX_SHADER, vertex_shader_source);\n",
              "    const fragment_shader = compile_shader(\n",
              "      gl, gl.FRAGMENT_SHADER, fragment_shader_source);\n",
              "\n",
              "    const shader_program = gl.createProgram();\n",
              "    gl.attachShader(shader_program, vertex_shader);\n",
              "    gl.attachShader(shader_program, fragment_shader);\n",
              "    gl.linkProgram(shader_program);\n",
              "\n",
              "    if (!gl.getProgramParameter(shader_program, gl.LINK_STATUS)) {\n",
              "      alert(\n",
              "        'Unable to initialize shader program: ' + \n",
              "        gl.getProgramInfoLog(shader_program)\n",
              "        );\n",
              "        return null;\n",
              "    }\n",
              "    return shader_program;\n",
              "  }\n",
              "\n",
              "  function compile_shader(gl, type, source) {\n",
              "    const shader = gl.createShader(type);\n",
              "    gl.shaderSource(shader, source);\n",
              "    gl.compileShader(shader);\n",
              "\n",
              "    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n",
              "      alert('An error occured compiling shader: ' + gl.getShaderInfoLog(shader));\n",
              "      gl.deleteShader(shader);\n",
              "      return null;\n",
              "    }\n",
              "\n",
              "    return shader;\n",
              "  }\n",
              "\n",
              "  // UI\n",
              "\n",
              "  var DRAG_START;\n",
              "  var DRAG_CURRENT;\n",
              "  var DRAGGING = false;\n",
              "\n",
              "  CANVAS.addEventListener('mousedown', function(e) {\n",
              "    DRAG_START = [e.offsetX, e.offsetY];\n",
              "    DRAGGING = true;\n",
              "  });\n",
              "\n",
              "  CANVAS.addEventListener('mousemove', function(e) {\n",
              "    DRAG_CURRENT = [e.offsetX, e.offsetY];\n",
              "  });\n",
              "\n",
              "  CANVAS.addEventListener('mouseup', function(e) {\n",
              "    const delta = get_drag_offset();\n",
              "    if (DIMENSION == 2) {\n",
              "      SCREEN_POSITION = [SCREEN_POSITION[0] + delta[0],\n",
              "                         SCREEN_POSITION[1] + delta[1]];\n",
              "    } else if (DIMENSION == 3) {\n",
              "      YAW -= delta[0];\n",
              "      PITCH -= delta[1];\n",
              "\n",
              "      if (PITCH > Math.PI / 2.1)\n",
              "        PITCH = Math.PI / 2.1;\n",
              "      if (PITCH < -Math.PI / 2.1)\n",
              "        PITCH = -Math.PI / 2.1;\n",
              "\n",
              "      make_look_at();\n",
              "    }\n",
              "\n",
              "    DRAGGING = false;\n",
              "  });\n",
              "\n",
              "  function toggle_play() {\n",
              "    IS_PLAYING = !IS_PLAYING;\n",
              "    console.log(PLAY_PAUSE_BUTTON);\n",
              "    if(IS_PLAYING)\n",
              "      PLAY_PAUSE_BUTTON.innerHTML = '||';\n",
              "    else\n",
              "      PLAY_PAUSE_BUTTON.innerHTML = '>';\n",
              "  }\n",
              "\n",
              "  function change_frame(value) {\n",
              "    if (!IS_LOADED)\n",
              "      return;\n",
              "    CURRENT_FRAME = value;\n",
              "    FRAME_RANGE.innerHTML = value;\n",
              "  }\n",
              "\n",
              "  function get_drag_offset() {\n",
              "    var delta = [DRAG_START[0] - DRAG_CURRENT[0],\n",
              "                 -DRAG_START[1] + DRAG_CURRENT[1]];\n",
              "    delta = [delta[0] / canvas.width * 2, delta[1] / canvas.height * 2];\n",
              "    if (DIMENSION == 2) {\n",
              "      delta = [delta[0] * CAMERA_SIZE[0],\n",
              "               delta[1] * CAMERA_SIZE[1]];\n",
              "    }\n",
              "    return delta;\n",
              "  }\n",
              "\n",
              "  const SCALE_SPEED = 0.1;\n",
              "  CANVAS.addEventListener('mousewheel', function(e) {\n",
              "    var delta = Math.sign(e.wheelDeltaY);\n",
              "    if (navigator.appVersion.indexOf('Mac'))\n",
              "      delta *= -0.1;\n",
              "    if (DIMENSION == 2) {\n",
              "      CAMERA_SIZE[0] = CAMERA_SIZE[0] * (1 + delta * SCALE_SPEED);\n",
              "      CAMERA_SIZE[1] = CAMERA_SIZE[1] * (1 + delta * SCALE_SPEED);\n",
              "    } else if (DIMENSION == 3) {\n",
              "      VIEW_DISTANCE = VIEW_DISTANCE * (1 + delta * SCALE_SPEED);\n",
              "      make_look_at();\n",
              "    }\n",
              "    e.preventDefault();\n",
              "  }, false);\n",
              "  CANVAS.addEventListener('DOMMouseScroll', function(e) {\n",
              "    const delta = Math.sign(e.detail);\n",
              "    if (DIMENSION == 2) {\n",
              "      CAMERA_SIZE[0] = CAMERA_SIZE[0] * (1 + delta * SCALE_SPEED);\n",
              "      CAMERA_SIZE[1] = CAMERA_SIZE[1] * (1 + delta * SCALE_SPEED);\n",
              "    } else if (DIMENSION == 3) {\n",
              "      VIEW_DISTANCE = VIEW_DISTANCE * (1 + delta * SCALE_SPEED);\n",
              "      make_look_at();\n",
              "    }\n",
              "    e.preventDefault();\n",
              "  }, false);\n",
              "\n",
              "\n",
              "  // SERIALIZATION UTILITIES\n",
              "  function decode(sBase64, nBlocksSize) {\n",
              "    var chrs = atob(sBase64);\n",
              "    var array = new Uint8Array(new ArrayBuffer(chrs.length));\n",
              "\n",
              "    for(var i = 0 ; i < chrs.length ; i++) {\n",
              "      array[i] = chrs.charCodeAt(i);\n",
              "    }\n",
              "\n",
              "    return new Float32Array(array.buffer);\n",
              "  }\n",
              "\n",
              "  function from_json(data) { \n",
              "    data = data.data['application/json'];\n",
              "\n",
              "    if (typeof data == 'string') {\n",
              "      return JSON.parse(data);\n",
              "    }\n",
              "\n",
              "    return data;\n",
              "  }\n",
              "\n",
              "  // RUN CELL\n",
              "\n",
              "  load_simulation();\n",
              "  update_frame();\n",
              "</script>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2cuLb1IQnnZo"
      },
      "source": [
        "## Larger Simulation with Neighbor Lists"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQQWnGuxnvZf"
      },
      "source": [
        "We can use neighbor lists to run a much larger version of this simulation. As their name suggests, neighbor lists are lists of particles nearby a central particle. By keeping track of neighbors, we can compute the energy of the system much more efficiently. This becomes increasingly true as the simulation gets larger. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Zzf9meloDql"
      },
      "source": [
        "N = 10000\n",
        "box_size = quantity.box_size_at_number_density(N, 0.8, 2)\n",
        "displacement, shift = space.periodic(box_size) \n",
        "\n",
        "kT = lambda t: np.where(t < 50.0, 0.1, 0.01)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcnnfeoQoDqm"
      },
      "source": [
        "As before we randomly initialize the system."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qEFpYkomoDqm"
      },
      "source": [
        "key, split = random.split(key)\n",
        "R = box_size * random.uniform(split, (N, dimension), dtype=np.float64)\n",
        "\n",
        "sigma = np.array([[1.0, 1.2], [1.2, 1.4]])\n",
        "N_2 = int(N / 2)\n",
        "species = np.where(np.arange(N) < N_2, 0, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJWuZXFfoDqm"
      },
      "source": [
        "Then we need to construct our simulation operators. This time we use the `energy.soft_sphere_neighbor_fn` to create two functions: one that constructs lists of neighbors and one that computes the energy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2O3OLWRaoDqm"
      },
      "source": [
        "neighbor_fn, energy_fn = energy.soft_sphere_neighbor_list(displacement,\n",
        "                                                          box_size,\n",
        "                                                          species=species, \n",
        "                                                          sigma=sigma)\n",
        "\n",
        "init, apply = simulate.nvt_nose_hoover(energy_fn, shift, dt, kT(0.), tau=200*dt)\n",
        "\n",
        "nbrs = neighbor_fn(R)\n",
        "state = init(key, R, neighbor=nbrs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xY4dnk53oDqm"
      },
      "source": [
        "Now let's actually do the simulation. This time our simulation step function will also update the neighbors. As above, we will also only record position data every hundred steps."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OibU3VOQoDqm"
      },
      "source": [
        "write_every = 100\n",
        "\n",
        "def step_fn(i, state_nbrs_log):\n",
        "  state, nbrs, log = state_nbrs_log\n",
        "\n",
        "  t = i * dt\n",
        "\n",
        "  # Log information about the simulation.\n",
        "  T = quantity.temperature(state.velocity)\n",
        "  log['kT'] = ops.index_update(log['kT'], i, T)\n",
        "  H = simulate.nvt_nose_hoover_invariant(energy_fn, state, kT(t), neighbor=nbrs)\n",
        "  log['H'] = ops.index_update(log['H'], i, H)\n",
        "  # Record positions every `write_every` steps.\n",
        "  log['position'] = lax.cond(i % write_every == 0,\n",
        "                             lambda p: ops.index_update(p, \n",
        "                                                        i // write_every, \n",
        "                                                        state.position),\n",
        "                             lambda p: p,\n",
        "                             log['position'])\n",
        "\n",
        "  # Take a simulation step.\n",
        "  state = apply(state, kT=kT(t), neighbor=nbrs)\n",
        "  nbrs = neighbor_fn(state.position, nbrs)\n",
        "\n",
        "  return state, nbrs, log"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7GyNMRbboDqm"
      },
      "source": [
        "To run our simulation we'll use `lax.fori_loop` which will execute the simulation a single call from python."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QGCAGy-ooDqm"
      },
      "source": [
        "steps = 20000\n",
        "\n",
        "log = {\n",
        "    'kT': np.zeros((steps,)),\n",
        "    'H': np.zeros((steps,)),\n",
        "    'position': np.zeros((steps // write_every,) + R.shape) \n",
        "}\n",
        "\n",
        "state, nbrs, log = lax.fori_loop(0, steps, step_fn, (state, nbrs, log))\n",
        "\n",
        "R = state.position"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87EOPGJPoDqm"
      },
      "source": [
        "Now, let's plot the temperature as a function of time. We see that the temperature tracks the goal temperature with some fluctuations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "QMIkiaO6oDqm",
        "outputId": "85833498-adc0-4c68-dacc-6cbb94255c2a"
      },
      "source": [
        "t = onp.arange(0, steps) * dt\n",
        "plt.plot(t, log['kT'], linewidth=3)\n",
        "plt.plot(t, kT(t), linewidth=3)\n",
        "format_plot('$t$', '$T$')\n",
        "finalize_plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rlq_kaeNoDqo"
      },
      "source": [
        "Now let's plot the Hamiltonian of the system. We see that it is invariant apart from changes to the temperature, as expected."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "LN5irdwUoDqo",
        "outputId": "0aac2adb-caed-413e-9ad5-a7b1254c6b78"
      },
      "source": [
        "plt.plot(t, log['H'], linewidth=3)\n",
        "format_plot('$t$', '$H$')\n",
        "finalize_plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W6Aeuz8xrpvuxSJImV6/3oG4Ezh8zdiewNjPfAPwIuBIgIk4BLgZObT7nDyKiPyL6gU8BFwCnAJc020qSZrCeBioz7wa2jRn7P5k51CzeA6xqrq8DbsnMPZn5MLAROKu5bMzMhzJzELil2VaSNIP1eg/qYH4Z+F/N9ZXAY13rNjVjE41LkmawsoGKiN8ChoDP9XoukqTp19OTJCYSEe+lc/LEz2ZmuxneDJzYtdmqZowXGZckzVDlAtWckfcR4O9m5q6uVbcBn4+ITwAnAGuAbwEtYE1ErKYTpouBd03vrCVJk63Xp5nfDJwLrIiITcBVdM7aWwDcGREA92TmBzNzQ0R8AXiAzqG/yzNzuLmdK4A76Jxmvj4zN0z7g5EkTaqeBiozLxln+IYX2f5q4Opxxm8Hbp/EqUmSeqzsSRKSpLnNQEmSSjJQkqSSDJQkqSQDJUkqyUBJkkoyUJKkkgyUJKkkAyVJKslASZJKMlCSpJIMlCSpJAMlSSrJQEmSSjJQkqSSDJQkqSQDJUkqyUBJkkoyUJKkkgyUJKkkAyVJKslASZJKMlCSpJIMlCSpJAMlSSrJQEmSSjJQkqSSDJQkqSQDJUkqyUBJkkoyUJKkkgyUJKkkAyVJKslASZJKMlCSpJIMlCSpJAMlSSrJQEmSShro9QSk2abdbjM80ma43WZkBIab5aULOv/cnt87TBvY03wcabehDbRgxZIFPLlzD8Mjbdpdt7l4Xj9P7dzD83tHAHhix/PT/bCkaWegXoaRkTbfefQZnt01eMC6vcNtNj+7i9euWMpAf4uBvj76+1q0WvDjrTtZvngeLVqHdH8LBvp460lHcVc+ydafPs+DW3awaF4/q1csoQ2M/nxrtzs/0jrL7a7xF5Zpthtd982Hn+bwRfN4/XGH0W7DimXz2T04zO7B4f3m0Boz5Y1bd7J25eGMtNusXrGUh5/ayZ0P/IRtzw3yc6ccy47dQywY6GPH7r2dH8Bd9l/qzO9L33+ck45eygVrj+OuHz3JMcsW7rvf0btutTr3+6/eEfznL29kcHhkv6/k2NvduHXnvuuL5vXzumOXHvC1HRxu8+CWHRy5ZD7HLFvASLvNSPuFzz180TyWzO+n1XwBWi3Y9Mzufd+XpQsGePq5A58Hkl45A/UyfPR/3M8t336s19OYVLff/8Qhf84X//Lxccf/6msPvaw5/HjrTn78lY3N0vYJt/vgH997yLe9e+8w39s08W1ue26QbeOEZvvuvWzfvXfcz9kzNMKeod7H6djDFvR6CtKUMFAvQ/dv5tJ4+lrQ39eir9Viz9DIhNsduWQ+fS14aueBoTv+8IW0gMe37384b+3Kw/btha9deRjrzlg5qXOXqjBQL8PHfn4tN3z9YZ7dtf9v1sMjI3w1n9y3fM7rjmZ4ZISh4TbffHjbvvEL1h53wCGziXzlh1v3ve4waumCAVrAOXE0K5bM3+/wU4vWvsNirRa0Ws2PsnHW/WDzDr72o858Tz3hMDY8vmO/+7nivJOBzuHBUUPDbT57z6McsWjeAT84u5226nBOPmYZfS149VGLWbl80X7rR3/AtmlzVz7Jn3XtjZ3xqiNYumCAd5/9al44OtjmY196kHa7vd/9XveLZ3LSMUu7brdjz9AIjz69i0eefo6h4TYrls3n1BMOP2CeT2zfzZM/3QOtFm981fImKvDdR5/hqZ17WLvycE4+ZmnX4VG49b5NPLdniAt+5nh27Rnm8Wd3c99jz3LcYQt582uW89aTjtr3PRl10188wh/f8ygLBvr5w196I8sWDnS+j13b7R4cZtMzuzh62QKOWDx/33i73Wbj1p0sGOjnVUctnvBrLs02rXZ77JH7ueGd73xn+9Zbb52S2x4eadPfd2ivM72Y3YPDfPaeRzj2sIWsO31qf1t+dtcg7TYsXzL/4Bs3hoZHmtfZJu8xH8wT25/nsEUDLJ7v71jSTBcR383MN40d91/3FJjMOAEsmt/PZeecNKm3OZHu39xfqoH+6X+3wnGHL5z2+5Q0vXwflCSpJAMlSSrJQEmSSjJQkqSSDJQkqSQDJUkqyUBJkkoyUJKkkgyUJKkkAyVJKslASZJKMlCSpJIMlCSpJAMlSSppzv65jQ0bNjwVEY/2eh6SJF493uCc/YOFkqTaPMQnSSrJQEmSSjJQkqSSDJQkqSQDJUkqyUBJkkqas++DeiUi4nzgWqAfuD4zr+nxlKZcRJwI3AQcC7SBT2fmtRFxJPDfgNcAjwD/JDOf6dU8p1pE9APfATZn5kURsRq4BTgK+C7wS5k52Ms5TqWIOAK4HlhL53nwy0AyR54DEfHrwK/Qeez3A+8DjmcWPwciYj1wEbA1M9c2Y+P+u4+IFp2fjRcCu4D3Zua9L/e+3YM6RM0PqE8BFwCnAJdExCm9ndW0GAJ+IzNPAc4GLm8e978GvpyZa4AvN8uz2T8HHuxa/g/AJzPzZOAZ4P09mdX0uRb435n5euA0Ol+LOfEciIiVwIeBNzU/qPuBi5n9z4EbgfPHjE30Pb8AWNNcLgOueyV3bKAO3VnAxsx8qPkt6RZgXY/nNOUyc8vob0KZ+VM6P5hW0nnsn2k2+wzw872Z4dSLiFXA36ezB0Hz2+LbgD9tNpntj/9w4BzgBoDMHMzMZ5lDzwE6R50WRcQAsBjYwix/DmTm3cC2McMTfc/XATdlZjsz7wGOiIjjX+59G6hDtxJ4rGt5UzM2Z0TEa4AzgG8Cx2bmlmbVE3QOAc5W/wn4CDDSLB8FPJuZQ83ybH8urAaeBP5rRNwXEddHxBLmyHMgMzcDHwf+mk6YttM5pDeXngOjJvqeT+rPRwOlQxIRS4H/DvyLzNzRvS4z23SOzc86ETF6DP67vZ5LDw0AZwLXZeYZwHOMOZw3y58Dy+nsIawGTgCWcOChrzlnKr/nBurQbQZO7Fpe1YzNehExj06cPpeZtzbDPxndhW8+bu3V/KbY3wL+YUQ8Quew7tvovB5zRHO4B2b/c2ETsCkzv9ks/ymdYM2V58DPAQ9n5pOZuRe4lc7zYi49B0ZN9D2f1J+PBurQfRtYExGrI2I+nRdJb+vxnKZc83rLDcCDmfmJrlW3AZc21y8F/my65zYdMvPKzFyVma+h8z3/Smb+IvBV4B83m83axw+QmU8Aj0VENEM/CzzAHHkO0Dm0d3ZELG7+PYw+/jnzHOgy0ff8NuA9EdGKiLOB7V2HAg+Z/5v5yxARF9J5PaIfWJ+ZV/d4SlMuIv428H/pnFo7+hrMR+m8DvUF4FXAo3RONx37guqsEhHnAv+yOc38tXT2qI4E7gPenZl7ejm/qRQRp9M5SWQ+8BCd06z7mCPPgYj4d8A/pXNW6310TjlfySx+DkTEzcC5wArgJ8BVwBcZ53vehPv36Rz63AW8LzO/83Lv20BJkkryEJ8kqSQDJUkqyUBJkkoyUJKkkgyUJKkkAyVJKslASTNURPx6RLQj4l29nos0FQyUNHO9sfk4l/9/QM1iBkqaud4I7AR+1OuJSFPB/0lCmmEi4hrgNydY/Z7M/Ox0zkeaKv7Jd2nmuZfOH4m7FPgGcGfXurt6MSFpKrgHJc1AEXEZ8IfABzLz072ejzQVfA1KmpnObD7e29NZSFPIQEkz05nAXjp//kSalQyUNMM0f731Z4AHZtPfHZLGMlDSzHMKsBAP72mWM1DSzHN68/G+ns5CmmIGSpp5jmo+7ujpLKQp5vugpJln9L82ujoi1gLPARsy8096OCdp0rkHJc0wmXk38CE6YfoQcBUvHPaTZg3fqCtJKsk9KElSSQZKklSSgZIklWSgJEklGShJUkkGSpJUkoGSJJVkoCRJJRkoSVJJ/x+PgLg3XFF3pwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EoG1KAFyoDqo"
      },
      "source": [
        "Now let's plot a snapshot of the system."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 873
        },
        "id": "JfV79m3ioDqo",
        "outputId": "345d68de-6000-4c4d-f8e1-1942d921f3d5"
      },
      "source": [
        "ms = 10\n",
        "R_plt = onp.array(state.position)\n",
        "\n",
        "plt.plot(R_plt[:N_2, 0], R_plt[:N_2, 1], 'o', markersize=ms * 0.5)\n",
        "plt.plot(R_plt[N_2:, 0], R_plt[N_2:, 1], 'o', markersize=ms * 0.7)\n",
        "\n",
        "plt.xlim([0, np.max(R[:, 0])])\n",
        "plt.ylim([0, np.max(R[:, 1])])\n",
        "\n",
        "plt.axis('off')\n",
        "\n",
        "finalize_plot((2, 2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmtKpLp6oDqo"
      },
      "source": [
        "If we want, we can also draw an animation of the simulation using JAX MD's renderer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "id": "TFz5LNV5oDqo",
        "outputId": "88df7d41-0d28-41be-a1b8-37c35fbd1eb2"
      },
      "source": [
        "from jax_md.colab_tools import renderer\n",
        "\n",
        "diameters = sigma[species, species]\n",
        "colors = np.where(species[:, None], \n",
        "                  np.array([[1.0, 0.5, 0.01]]), \n",
        "                  np.array([[0.35, 0.65, 0.85]]))\n",
        "\n",
        "renderer.render(box_size,\n",
        "                {\n",
        "                    'particles': renderer.Disk(log['position'], \n",
        "                                               diameters,\n",
        "                                               colors)\n",
        "                },\n",
        "                buffer_size=20,\n",
        "                resolution=(700, 700))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<!--\n",
              "  Copyright 2020 Google LLC\n",
              "  Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "  you may not use this file except in compliance with the License.\n",
              "  You may obtain a copy of the License at\n",
              "      https://www.apache.org/licenses/LICENSE-2.0\n",
              "  Unless required by applicable law or agreed to in writing, software\n",
              "  distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "  See the License for the specific language governing permissions and\n",
              "  limitations under the License.\n",
              "-->\n",
              "\n",
              "<!--\n",
              "  A fragment of HTML and Javascript that describes a visualization tool.\n",
              "  \n",
              "  This code is expected to be injected into Jupyter or Colaboratory notebooks using the `IPython.display.HTML` function. The tool is rendered using WebGL2.\n",
              "-->\n",
              "\n",
              "<div id='seek'>\n",
              "  <button type='button'\n",
              "          id='pause_play' \n",
              "          style='width:40px; vertical-align:middle;' \n",
              "          onclick=\"toggle_play()\"> || \n",
              "  </button>\n",
              "  <input type=\"range\" \n",
              "         min=\"0\"\n",
              "         max=\"1\"\n",
              "         value=\"0\"\n",
              "         style=\"width:512px; vertical-align:middle;\"\n",
              "         class=\"slider\"\n",
              "         id=\"frame_range\"\n",
              "         oninput='change_frame(this.value)'>\n",
              "</div>\n",
              "<canvas id=\"canvas\"></canvas>\n",
              "<div id='info'> </div>\n",
              "<div id='error' style=\"color:red\"> </div>\n",
              "<script src=\"https://cdnjs.cloudflare.com/ajax/libs/gl-matrix/2.8.1/gl-matrix-min.js\"></script>\n",
              "\n",
              "<script>\n",
              "  var DIMENSION;\n",
              "\n",
              "  var SIZE;\n",
              "\n",
              "  var SHAPE = {};\n",
              "\n",
              "  var GEOMETRY = {};\n",
              "\n",
              "  var CURRENT_FRAME = 0;\n",
              "  var FRAME_COUNT = 0;\n",
              "\n",
              "  var BOX_SIZE;\n",
              "  var READ_BUFFER_SIZE = null;\n",
              "  var IS_LOADED = false;\n",
              "  var SIMULATION_IDX = 0;\n",
              "\n",
              "  // Info\n",
              "\n",
              "  var INFO = document.getElementById('info');\n",
              "  var ERROR = document.getElementById('error');\n",
              "\n",
              "  // Graphics\n",
              "\n",
              "  var GL;\n",
              "  var SHADER;\n",
              "  var BACKGROUND_COLOR = [0.2, 0.2, 0.2];\n",
              "\n",
              "  // 3D Camera\n",
              "\n",
              "  var EYE = mat4.create();\n",
              "  var PERSPECTIVE = mat4.create();\n",
              "  var LOOK_AT = mat4.create()\n",
              "  var YAW = 0.0;\n",
              "  var PITCH = 0.0;\n",
              "  var CAMERA_POSITION = mat4.create();\n",
              "  var Y_ROTATION_MATRIX = mat4.create();\n",
              "  var X_ROTATION_MATRIX = mat4.create();\n",
              "  var VIEW_DISTANCE = 0.0;\n",
              "\n",
              "  function make_look_at() {\n",
              "    var center = [BOX_SIZE[0] / 2.0, BOX_SIZE[1] / 2.0, BOX_SIZE[2] / 2.0];\n",
              "    var direction = [Math.cos(YAW) * Math.cos(PITCH),\n",
              "                     Math.sin(PITCH),\n",
              "                     Math.sin(YAW) * Math.cos(PITCH)];\n",
              "    var pos = [center[0] - VIEW_DISTANCE * direction[0],\n",
              "               center[1] - VIEW_DISTANCE * direction[1],\n",
              "               center[2] - VIEW_DISTANCE * direction[2]];\n",
              "    mat4.lookAt(LOOK_AT, \n",
              "                pos, \n",
              "                center, \n",
              "                [0.0, 1.0, 0.0]);\n",
              "  }\n",
              "\n",
              "  // 2D Camera\n",
              "\n",
              "  var SCREEN_POSITION = [0, 0];\n",
              "  var CAMERA_SIZE = [0, 0];\n",
              "\n",
              "  // Bonds\n",
              "\n",
              "  const BOND_SEGMENTS = 3;\n",
              "  const VERTICES_PER_BOND = BOND_SEGMENTS * 6;\n",
              "\n",
              "  // Simulation State\n",
              "\n",
              "  var IS_PLAYING = true;\n",
              "  var PLAY_PAUSE_BUTTON = document.getElementById('pause_play');\n",
              "\n",
              "  var FRAME_RANGE = document.getElementById('frame_range');\n",
              "\n",
              "  google.colab.output.setIframeHeight(0, true, {maxHeight: 5000});\n",
              "  var invokeFunction = google.colab.kernel.invokeFunction;\n",
              "\n",
              "  var CANVAS = document.getElementById(\"canvas\");\n",
              "  CANVAS.width = 1024;\n",
              "  CANVAS.height = 1024;\n",
              "\n",
              "  // Simulation Loading.\n",
              "\n",
              "  function make_sizes() {\n",
              "    return {\n",
              "      position: DIMENSION,\n",
              "      angle: DIMENSION - 1,\n",
              "      size: 1,\n",
              "      color: 3,\n",
              "    };\n",
              "  }\n",
              "\n",
              "  function simulation_info_string() {\n",
              "    return ('<p style=\"color:yellow\">' +\n",
              "            'Simulation Info:</p><div style=\"padding-left: 20px; padding-bottom: 10px;\">' +\n",
              "            'Box Size:    ' + BOX_SIZE.map(x => parseFloat(x).toFixed(2)) + '<br>' +\n",
              "            'Dimension:   ' + DIMENSION + '<br>' +\n",
              "            'Frame Count: ' + FRAME_COUNT + '<br></div>');\n",
              "  }\n",
              "\n",
              "  async function load_simulation() {\n",
              "    console.log(\"Loading simulation.\"); \n",
              "    INFO.innerHTML = 'Loading simulation...<br>';\n",
              "\n",
              "    var result = await invokeFunction('GetSimulationMetadata', [], {});\n",
              "    var metadata = from_json(result);\n",
              "\n",
              "    if(!metadata.box_size) {\n",
              "      ERROR.innerHTML += 'ERROR: No box size specified.<br>';\n",
              "    }\n",
              "\n",
              "    FRAME_COUNT = metadata.frame_count;\n",
              "    BOX_SIZE = metadata.box_size;\n",
              "    DIMENSION = metadata.dimension;\n",
              "    SIMULATION_IDX = metadata.simulation_idx;\n",
              "\n",
              "    if (metadata.background_color)\n",
              "      BACKGROUND_COLOR = metadata.background_color;\n",
              "\n",
              "    if (metadata.resolution) {\n",
              "      CANVAS.width = metadata.resolution[0];\n",
              "      CANVAS.height = metadata.resolution[1];\n",
              "    }\n",
              "\n",
              "    const aspect_ratio = CANVAS.width / CANVAS.height;\n",
              "\n",
              "    INFO.innerHTML += simulation_info_string();\n",
              "\n",
              "    SIZE = make_sizes();\n",
              "    initialize_gl();\n",
              "\n",
              "    if (DIMENSION == 2) {\n",
              "      SCREEN_POSITION = [BOX_SIZE[0] / 2.0, BOX_SIZE[1] / 2.0];\n",
              "      CAMERA_SIZE = [aspect_ratio * BOX_SIZE[0] / 2.0, BOX_SIZE[1] / 2.0];\n",
              "    } else if (DIMENSION == 3) {\n",
              "      const fovy = 45.0 / 180.0 * Math.PI;\n",
              "      const max_box_size = Math.max(BOX_SIZE[0], BOX_SIZE[1], BOX_SIZE[2]);\n",
              "      PERSPECTIVE = mat4.perspective(PERSPECTIVE, \n",
              "                                     fovy,            // Field of view.\n",
              "                                     aspect_ratio,    // Aspect ratio.\n",
              "                                     max_box_size / 10.0, // Near clip plane.\n",
              "                                     100 * max_box_size); // Far clip plane.\n",
              "      VIEW_DISTANCE = 2 * max_box_size;\n",
              "      make_look_at();\n",
              "    } else {\n",
              "      ERROR.innerHTML += 'ERROR: Invalid dimension specified: ' + DIMENSION + '.<br>';\n",
              "    }\n",
              "\n",
              "    FRAME_RANGE.max = FRAME_COUNT - 1;\n",
              "\n",
              "    // This specifies the maximum number of frames of data we will try to\n",
              "    // transfer between Python and Javascript at a time.\n",
              "    READ_BUFFER_SIZE = metadata.buffer_size;\n",
              "    if (!READ_BUFFER_SIZE)\n",
              "      READ_BUFFER_SIZE = FRAME_COUNT;\n",
              "\n",
              "    var geometry_list = metadata.geometry;\n",
              "    if (geometry_list) {\n",
              "      for (var i = 0; i < geometry_list.length ; i++) {\n",
              "        const name = geometry_list[i];\n",
              "        GEOMETRY[name] = await load_geometry(name);\n",
              "      }\n",
              "    }\n",
              "\n",
              "    IS_LOADED = true;\n",
              "  }\n",
              "\n",
              "  async function load_geometry(name) {\n",
              "    console.log('Loading ' + name + '.');\n",
              "    INFO.innerHTML += 'Loading geometry \"' + name + '\".<br>';\n",
              "    var result = await invokeFunction('GetGeometryMetadata' + SIMULATION_IDX,\n",
              "                                      [name], {});\n",
              "    var data = from_json(result);\n",
              "\n",
              "    console.log(data);\n",
              "\n",
              "    var geometry = {};\n",
              "    geometry.name = name;\n",
              "    geometry.shape = data.shape;\n",
              "    geometry.count = data.count;\n",
              "    geometry.selected = new Int32Array(data.count);\n",
              "\n",
              "    if (data.reference_geometry)\n",
              "      geometry.reference_geometry = data.reference_geometry;\n",
              "\n",
              "    if (data.max_neighbors)\n",
              "      geometry.max_neighbors = data.max_neighbors;\n",
              "\n",
              "    if(!geometry.shape) {\n",
              "      ERROR.innerHTML += 'ERROR: No shape specified for the geometry.<br>';\n",
              "    }\n",
              "\n",
              "    for (var field in data.fields) {\n",
              "      var array;\n",
              "      var array_type;\n",
              "      if (data.fields[field] == 'dynamic') {\n",
              "        array = await load_dynamic_array(name, field, geometry.count);\n",
              "        array_type = GL.DYNAMIC_DRAW;\n",
              "      } else if (data.fields[field] == 'static') {\n",
              "        array = await load_array(name, field);\n",
              "        array_type = GL.STATIC_DRAW;\n",
              "      } else if (data.fields[field] == 'global') {\n",
              "        array = await load_array(name, field);\n",
              "        array_type = 'GLOBAL';\n",
              "      } else {\n",
              "        ERROR.innerHTML += ('ERROR: field must have type \"dynamic\", \"static\", or ' +\n",
              "                            '\"global\". Found' + data.fields[field] + '.<br>');\n",
              "      }\n",
              "\n",
              "      geometry[field] = array;\n",
              "      geometry[field + '_type'] = array_type; \n",
              "\n",
              "      if (data.shape == 'Bond' && field == 'neighbor_idx')\n",
              "        continue;\n",
              "\n",
              "      if (array_type != 'GLOBAL') {\n",
              "        var array_buffer = GL.createBuffer();\n",
              "        var array_buffer_size = 4 * SIZE[field] * geometry.count;\n",
              "        GL.bindBuffer(GL.ARRAY_BUFFER, array_buffer);\n",
              "        GL.bufferData(GL.ARRAY_BUFFER, array, array_type);\n",
              "        geometry[field + '_buffer'] = array_buffer;  \n",
              "        geometry[field + '_buffer_size'] = array_buffer_size;   \n",
              "      }\n",
              "    }\n",
              "\n",
              "    if (data.shape == 'Bond') {\n",
              "      var vertex_buffer = GL.createBuffer();\n",
              "      var vertex_count = (geometry.count * \n",
              "                          geometry.max_neighbors * \n",
              "                          VERTICES_PER_BOND);\n",
              "      var vertex_buffer_size = 4 * SIZE['position'] * vertex_count;\n",
              "      GL.bindBuffer(GL.ARRAY_BUFFER, vertex_buffer);\n",
              "      GL.bufferData(GL.ARRAY_BUFFER, vertex_buffer_size, GL.DYNAMIC_DRAW);\n",
              "\n",
              "      geometry.vertices = new Float32Array(SIZE['position'] * vertex_count);\n",
              "      geometry.vertex_buffer = vertex_buffer;\n",
              "      geometry.vertex_buffer_size = vertex_buffer_size;\n",
              "\n",
              "      var vertex_normal_buffer = GL.createBuffer();\n",
              "      GL.bindBuffer(GL.ARRAY_BUFFER, vertex_normal_buffer);\n",
              "      GL.bufferData(GL.ARRAY_BUFFER, vertex_buffer_size, GL.DYNAMIC_DRAW);\n",
              "\n",
              "      geometry.normals = new Float32Array(SIZE['position'] * vertex_count);\n",
              "      geometry.vertex_normal_buffer = vertex_normal_buffer;\n",
              "    }\n",
              "\n",
              "    return geometry;\n",
              "  }\n",
              "\n",
              "  async function load_dynamic_array(name, field, count) {\n",
              "    if (!FRAME_COUNT) {\n",
              "      ERROR.innerHTML += 'ERROR: No frame count specified.<br>';\n",
              "    }\n",
              "\n",
              "    var array = new Float32Array(FRAME_COUNT * count * SIZE[field]);\n",
              "\n",
              "    const info_text = INFO.innerHTML;\n",
              "\n",
              "    for (var i = 0 ; i < FRAME_COUNT ; i += READ_BUFFER_SIZE) {\n",
              "      await load_array_chunk(name, field, count, array, i, info_text);\n",
              "    }\n",
              "\n",
              "    INFO.innerHTML = info_text + 'Loaded \"' + field + '\".<br>';\n",
              "\n",
              "    return array;\n",
              "  }\n",
              "\n",
              "  async function load_array_chunk(name, field, count, array, offset, info_text) {\n",
              "    var dbg_string = ('Loading \"' + field + '\" chunk at time offset ' + offset +\n",
              "                      '.<br>'); \n",
              "    console.log(dbg_string);\n",
              "    INFO.innerHTML = info_text + dbg_string + '<br>';\n",
              "\n",
              "    var result = await invokeFunction('GetArrayChunk' + SIMULATION_IDX, \n",
              "                                      [name, field, offset, READ_BUFFER_SIZE],\n",
              "                                      {});\n",
              "    var data = from_json(result);\n",
              "\n",
              "    if (!data.array_chunk) {\n",
              "      // Error checking.\n",
              "    }\n",
              "\n",
              "    var array_chunk = decode(data.array_chunk);\n",
              "    array.set(array_chunk, offset * count * SIZE[field]);\n",
              "  }\n",
              "\n",
              "  async function load_array(name, field) {\n",
              "    const info_text = INFO.innerHTML;\n",
              "    INFO.innerHTML += 'Loading \"' + field + '\".<br>';\n",
              "    var result = await invokeFunction('GetArray' + SIMULATION_IDX,\n",
              "                                      [name, field], {});\n",
              "    var data = from_json(result);\n",
              "\n",
              "    if (!data.array) {\n",
              "      ERROR.innerHTML += 'ERROR: No data array specified.<br>';\n",
              "    }\n",
              "    INFO.innerHTML = info_text + 'Loaded \"' + field + '\".<br>';\n",
              "    return decode(data.array);\n",
              "  }\n",
              "\n",
              "  function initialize_gl() {\n",
              "    GL = CANVAS.getContext(\"webgl2\");\n",
              "\n",
              "    if (!GL) {\n",
              "        alert('Unable to initialize WebGL.');\n",
              "        return;\n",
              "    }\n",
              "\n",
              "    GL.viewport(0, 0, GL.drawingBufferWidth, GL.drawingBufferHeight);\n",
              "\n",
              "    if (BACKGROUND_COLOR)\n",
              "      GL.clearColor(BACKGROUND_COLOR[0], \n",
              "                    BACKGROUND_COLOR[1], \n",
              "                    BACKGROUND_COLOR[2], \n",
              "                    1.0);\n",
              "    else\n",
              "      GL.clearColor(0.2, 0.2, 0.2, 1.0);\n",
              "    GL.enable(GL.DEPTH_TEST);\n",
              "\n",
              "    var shader_program;\n",
              "    if (DIMENSION == 2)\n",
              "      shader_program = initialize_shader(\n",
              "          GL, VERTEX_SHADER_SOURCE_2D, FRAGMENT_SHADER_SOURCE_2D);\n",
              "    else if (DIMENSION == 3)\n",
              "      shader_program = initialize_shader(\n",
              "          GL, VERTEX_SHADER_SOURCE_3D, FRAGMENT_SHADER_SOURCE_3D);\n",
              "\n",
              "    SHADER = {\n",
              "      program: shader_program,\n",
              "      attribute: {\n",
              "          vertex_position: GL.getAttribLocation(shader_program, 'vertex_position'),\n",
              "          position: GL.getAttribLocation(shader_program, 'position'),\n",
              "          size: GL.getAttribLocation(shader_program, 'size'),\n",
              "          color: GL.getAttribLocation(shader_program, 'color'),\n",
              "      },\n",
              "      uniform: {\n",
              "          color: GL.getUniformLocation(shader_program, 'color'),\n",
              "          global_size: GL.getUniformLocation(shader_program, 'global_size'),\n",
              "          use_global_size: GL.getUniformLocation(shader_program, 'use_global_size'),\n",
              "          global_color: GL.getUniformLocation(shader_program, 'global_color'),\n",
              "          use_global_color: GL.getUniformLocation(shader_program, 'use_global_color'),\n",
              "          use_position: GL.getUniformLocation(shader_program, 'use_position')\n",
              "      },\n",
              "    };\n",
              "\n",
              "    if (DIMENSION == 2) {\n",
              "      SHADER.uniform['screen_position'] = GL.getUniformLocation(shader_program, 'screen_position');\n",
              "      SHADER.uniform['screen_size'] = GL.getUniformLocation(shader_program, 'screen_size');\n",
              "    } else if (DIMENSION == 3) {\n",
              "      SHADER.attribute['vertex_normal'] = GL.getAttribLocation(shader_program, 'vertex_normal');\n",
              "      SHADER.uniform['perspective'] = GL.getUniformLocation(shader_program, 'perspective');\n",
              "      SHADER.uniform['light_direction'] = GL.getUniformLocation(shader_program, 'light_direction');\n",
              "    }\n",
              "\n",
              "    GL.useProgram(shader_program);\n",
              "    \n",
              "    GL.uniform4f(SHADER.uniform.color, 0.9, 0.9, 1.0, 1.0);\n",
              "    GL.uniform1f(SHADER.uniform.global_size, 1.0);\n",
              "    GL.uniform1i(SHADER.uniform.use_global_size, true);\n",
              "\n",
              "    GL.uniform3f(SHADER.uniform.global_color, 1.0, 1.0, 1.0);\n",
              "    GL.uniform1i(SHADER.uniform.use_global_color, true);\n",
              "    GL.uniform1f(SHADER.uniform.use_position, 1.0);\n",
              "\n",
              "    var inorm = 1.0 / Math.sqrt(3);\n",
              "    GL.uniform3f(SHADER.uniform.light_direction, inorm, -inorm, inorm)\n",
              "\n",
              "    SHAPE['Disk'] = make_disk(GL, SHADER, 16, 0.5);\n",
              "    SHAPE['Sphere'] = make_sphere(GL, SHADER, 16, 16, 0.5);\n",
              "\n",
              "    const vao = GL.createVertexArray();\n",
              "    GL.bindVertexArray(vao);\n",
              "  }\n",
              "\n",
              "  function make_disk(gl, shader, segments, radius) {\n",
              "    var position = new Float32Array(segments * DIMENSION * 3);\n",
              "    for (var s = 0 ; s < segments ; s++) {\n",
              "        const th = 2 * s / segments * Math.PI;\n",
              "        const th_p = 2 * (s + 1) / segments * Math.PI;\n",
              "        position.set([0.0, 0.0], s * 3 * DIMENSION);\n",
              "        position.set([radius * Math.cos(th), radius * Math.sin(th)],\n",
              "                     s * 3 * DIMENSION + DIMENSION);\n",
              "        position.set([radius * Math.cos(th_p), radius * Math.sin(th_p)], \n",
              "                     s * 3 * DIMENSION + 2 * DIMENSION);\n",
              "    }\n",
              "\n",
              "    var buffer = gl.createBuffer();\n",
              "    gl.bindBuffer(gl.ARRAY_BUFFER, buffer);\n",
              "    gl.bufferData(gl.ARRAY_BUFFER, position, gl.STATIC_DRAW);\n",
              "\n",
              "    return {\n",
              "      vertex_position: position,\n",
              "      vertex_buffer: buffer,\n",
              "      vertex_count: segments * 3,\n",
              "    };\n",
              "  }\n",
              "\n",
              "  function make_sphere(gl, shader, horizontal_segments, vertical_segments, radius) {\n",
              "    const stride = DIMENSION * 3 * 2;  // 3 vertices per tri, two tris per quad.\n",
              "    if (DIMENSION != 3) {\n",
              "      return null;\n",
              "      // Error Checking.\n",
              "    }\n",
              "\n",
              "    var position = new Float32Array(vertical_segments * horizontal_segments * stride);\n",
              "    var normal = new Float32Array(vertical_segments * horizontal_segments * stride);\n",
              "\n",
              "    var dtheta = 2 * Math.PI / horizontal_segments;\n",
              "    var dphi = Math.PI / vertical_segments;\n",
              "\n",
              "    for (var vs = 0 ; vs < vertical_segments ; vs++) {\n",
              "      const phi_0 = vs * dphi;\n",
              "      const phi_1 = (vs + 1) * dphi;\n",
              "      const offset = vs * horizontal_segments * stride;\n",
              "      for (var hs = 0 ; hs < horizontal_segments ; hs++) {\n",
              "        const theta_0 = hs * dtheta;\n",
              "        const theta_1 = (hs + 1) * dtheta;\n",
              "\n",
              "        const x0 = radius * Math.sin(phi_0) * Math.cos(theta_0);\n",
              "        const y0 = radius * Math.sin(phi_0) * Math.sin(theta_0);\n",
              "        const z0 = radius * Math.cos(phi_0);\n",
              "\n",
              "        const x1 = radius * Math.sin(phi_1) * Math.cos(theta_0);\n",
              "        const y1 = radius * Math.sin(phi_1) * Math.sin(theta_0);\n",
              "        const z1 = radius * Math.cos(phi_1);\n",
              "\n",
              "        const x2 = radius * Math.sin(phi_0) * Math.cos(theta_1);\n",
              "        const y2 = radius * Math.sin(phi_0) * Math.sin(theta_1);\n",
              "        const z2 = radius * Math.cos(phi_0);\n",
              "\n",
              "        const x3 = radius * Math.sin(phi_1) * Math.cos(theta_1);\n",
              "        const y3 = radius * Math.sin(phi_1) * Math.sin(theta_1);\n",
              "        const z3 = radius * Math.cos(phi_1);\n",
              "\n",
              "        position.set([x0, y0, z0,\n",
              "                      x1, y1, z1,\n",
              "                      x2, y2, z2,\n",
              "                      x1, y1, z1,\n",
              "                      x3, y3, z3,\n",
              "                      x2, y2, z2], offset + hs * stride);\n",
              "\n",
              "        normal.set([x0 / radius, y0 / radius, z0 / radius,\n",
              "                    x1 / radius, y1 / radius, z1 / radius,\n",
              "                    x2 / radius, y2 / radius, z2 / radius,\n",
              "                    x1 / radius, y1 / radius, z1 / radius,\n",
              "                    x3 / radius, y3 / radius, z3 / radius,\n",
              "                    x2 / radius, y2 / radius, z2 / radius], offset + hs * stride);              \n",
              "      }\n",
              "    }\n",
              "\n",
              "    var buffer = gl.createBuffer();\n",
              "    gl.bindBuffer(gl.ARRAY_BUFFER, buffer);\n",
              "    gl.bufferData(gl.ARRAY_BUFFER, position, gl.STATIC_DRAW);\n",
              "\n",
              "    var normal_buffer = gl.createBuffer();\n",
              "    gl.bindBuffer(gl.ARRAY_BUFFER, normal_buffer);\n",
              "    gl.bufferData(gl.ARRAY_BUFFER, normal, gl.STATIC_DRAW);\n",
              "\n",
              "    return {\n",
              "      vertex_position: position,\n",
              "      vertex_buffer: buffer,\n",
              "      vertex_normals: normal,\n",
              "      vertex_normal_buffer: normal_buffer,\n",
              "      vertex_count: vertical_segments * horizontal_segments * 3 * 2\n",
              "    };\n",
              "  }\n",
              "\n",
              "  function set_attribute(geometry, name) {\n",
              "    if (!geometry[name]) {\n",
              "      if (SIZE[name] == 1)\n",
              "        GL.uniform1f(SHADER.uniform['global_' + name], 1.0);\n",
              "      else if (SIZE[name] == 2)\n",
              "        GL.uniform2f(SHADER.uniform['global_' + name], 1.0, 1.0);\n",
              "      else if (SIZE[name] == 3)\n",
              "        GL.uniform3f(SHADER.uniform['global_' + name], 1.0, 1.0, 1.0);\n",
              "\n",
              "      GL.uniform1i(SHADER.uniform['use_global_' + name], true);\n",
              "    } else if (geometry[name + '_type'] == 'GLOBAL') {\n",
              "      if (SIZE[name] == 1)\n",
              "        GL.uniform1fv(SHADER.uniform['global_' + name], geometry[name]);\n",
              "      else if (SIZE[name] == 2)\n",
              "        GL.uniform2fv(SHADER.uniform['global_' + name], geometry[name]);\n",
              "      else if (SIZE[name] == 3)\n",
              "        GL.uniform3fv(SHADER.uniform['global_' + name], geometry[name]);\n",
              "\n",
              "      GL.uniform1i(SHADER.uniform['use_global_' + name], true);\n",
              "    } else {\n",
              "      GL.enableVertexAttribArray(SHADER.attribute[name]);\n",
              "      var stride = SIZE[name] * geometry.count;\n",
              "      GL.bindBuffer(GL.ARRAY_BUFFER, geometry[name + '_buffer']);\n",
              "      if (geometry[name + '_type'] == GL.DYNAMIC_DRAW) {\n",
              "        const data = geometry[name].slice(CURRENT_FRAME * stride, \n",
              "                                          (CURRENT_FRAME + 1) * stride);\n",
              "        GL.bufferSubData(GL.ARRAY_BUFFER, 0, data);\n",
              "      }\n",
              "      GL.vertexAttribPointer(\n",
              "        SHADER.attribute[name],               \n",
              "        SIZE[name],        \n",
              "        GL.FLOAT,         \n",
              "        false,            \n",
              "        0,    \n",
              "        0\n",
              "      );                \n",
              "      GL.vertexAttribDivisor(SHADER.attribute[name], 1);\n",
              "\n",
              "      GL.uniform1i(SHADER.uniform['use_global_' + name], false);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  var loops = 0;\n",
              "\n",
              "  function update_frame() {\n",
              "    if(!GL) {\n",
              "      window.requestAnimationFrame(update_frame);\n",
              "      return;\n",
              "    }\n",
              "\n",
              "    GL.clear(GL.COLOR_BUFFER_BIT | GL.DEPTH_BUFFER_BIT);\n",
              "    \n",
              "    if (!IS_LOADED) {\n",
              "      window.requestAnimationFrame(update_frame);\n",
              "      return;\n",
              "    }\n",
              "\n",
              "    if (DIMENSION == 2) {\n",
              "      var camera_x = SCREEN_POSITION[0];\n",
              "      var camera_y = SCREEN_POSITION[1];\n",
              "\n",
              "      if (DRAGGING) {\n",
              "        const delta = get_drag_offset();\n",
              "        camera_x += delta[0];\n",
              "        camera_y += delta[1];\n",
              "      }\n",
              "      GL.uniform2f(SHADER.uniform.screen_position, camera_x, camera_y);\n",
              "      GL.uniform2f(SHADER.uniform.screen_size, CAMERA_SIZE[0], CAMERA_SIZE[1]);\n",
              "    } else if (DIMENSION == 3) {\n",
              "\n",
              "      // Now these are some janky globals.\n",
              "      if (DRAGGING) {\n",
              "        var yaw = YAW;\n",
              "        var pitch = PITCH;\n",
              "        const delta = get_drag_offset();\n",
              "        YAW = YAW - delta[0];\n",
              "        PITCH = PITCH - delta[1];\n",
              "        make_look_at();\n",
              "        YAW = yaw;\n",
              "        PITCH = pitch;\n",
              "      }\n",
              "\n",
              "      GL.uniformMatrix4fv(SHADER.uniform.perspective, false,\n",
              "                          mat4.multiply(EYE, PERSPECTIVE, LOOK_AT));\n",
              "    }\n",
              "\n",
              "    if (CURRENT_FRAME > FRAME_COUNT - 1) {\n",
              "      loops++;\n",
              "      CURRENT_FRAME = 0;\n",
              "    }\n",
              "\n",
              "    for (var name in GEOMETRY) {\n",
              "      var geom = GEOMETRY[name];\n",
              "\n",
              "      set_attribute(geom, 'size');\n",
              "      set_attribute(geom, 'color');\n",
              "\n",
              "      var shape = geom.shape;\n",
              "      var vertex_buffer;\n",
              "      var vertex_count;\n",
              "      var vertex_normal_buffer = null;\n",
              "\n",
              "      if (shape != 'Bond') {\n",
              "        shape = SHAPE[shape];\n",
              "\n",
              "        update_shape(geom);        \n",
              "\n",
              "        vertex_buffer = shape.vertex_buffer;\n",
              "        vertex_count = shape.vertex_count;\n",
              "        vertex_normal_buffer = shape.vertex_normal_buffer;\n",
              "      } else {\n",
              "        vertex_count = update_bond_vertex_data(GEOMETRY[geom.reference_geometry],\n",
              "                                               geom);\n",
              "        vertex_buffer = geom.vertex_buffer;\n",
              "        vertex_normal_buffer = geom.vertex_normal_buffer;\n",
              "      }\n",
              "\n",
              "      GL.enableVertexAttribArray(SHADER.attribute.vertex_position);\n",
              "      GL.bindBuffer(GL.ARRAY_BUFFER, vertex_buffer);\n",
              "      GL.vertexAttribPointer(\n",
              "        SHADER.attribute.vertex_position,\n",
              "        DIMENSION,\n",
              "        GL.FLOAT,\n",
              "        false,\n",
              "        0,\n",
              "        0\n",
              "      );\n",
              "\n",
              "      if (DIMENSION == 3 && vertex_normal_buffer) { \n",
              "        GL.enableVertexAttribArray(SHADER.attribute.vertex_normal);\n",
              "        GL.bindBuffer(GL.ARRAY_BUFFER, vertex_normal_buffer);\n",
              "        GL.vertexAttribPointer(\n",
              "          SHADER.attribute.vertex_normal,\n",
              "          DIMENSION,\n",
              "          GL.FLOAT,\n",
              "          false,\n",
              "          0,\n",
              "          0\n",
              "        );\n",
              "      }\n",
              "\n",
              "      if (shape == 'Bond')\n",
              "      {\n",
              "        GL.drawArrays(GL.TRIANGLES, 0, vertex_count);\n",
              "      }\n",
              "      else {\n",
              "        GL.drawArraysInstanced(GL.TRIANGLES, 0, vertex_count, geom.count);\n",
              "      }\n",
              "    }\n",
              "\n",
              "    if(IS_PLAYING) {\n",
              "      CURRENT_FRAME++;\n",
              "      FRAME_RANGE.value = CURRENT_FRAME;\n",
              "    }\n",
              "\n",
              "    window.requestAnimationFrame(update_frame);\n",
              "  }\n",
              "\n",
              "  function update_shape(geometry) {\n",
              "    GL.enableVertexAttribArray(SHADER.attribute.position);\n",
              "    var stride = geometry.count * DIMENSION;\n",
              "    GL.bindBuffer(GL.ARRAY_BUFFER, geometry.position_buffer);\n",
              "    if (geometry.position_type == GL.DYNAMIC_DRAW) {\n",
              "      const positions = geometry.position.subarray(CURRENT_FRAME * stride, \n",
              "                                                   (CURRENT_FRAME + 1) * stride);\n",
              "      GL.bufferSubData(GL.ARRAY_BUFFER, 0, positions);\n",
              "    }\n",
              "    GL.vertexAttribPointer(\n",
              "      SHADER.attribute.position,               \n",
              "      DIMENSION,        \n",
              "      GL.FLOAT,         \n",
              "      false,            \n",
              "      0,    \n",
              "      0\n",
              "    );                \n",
              "    GL.vertexAttribDivisor(SHADER.attribute.position, 1);\n",
              "    GL.uniform1f(SHADER.uniform.use_position, 1.0);\n",
              "  }\n",
              "\n",
              "  function update_bond_vertex_data(reference_geometry, bonds) {\n",
              "    const geom = reference_geometry;\n",
              "    const N = geom.count;\n",
              "    var stride = N * DIMENSION;\n",
              "    const positions = geom.position.subarray(CURRENT_FRAME * stride, \n",
              "                                             (CURRENT_FRAME + 1) * stride);\n",
              "    const neighbors = bonds.max_neighbors;\n",
              "\n",
              "    var vertex_count = 0;\n",
              "    var vertices = bonds.vertices;\n",
              "    var normals = bonds.normals;\n",
              "\n",
              "    for (var i = 0 ; i < N ; i++) {\n",
              "      var r_i = positions.subarray(i * DIMENSION, (i + 1) * DIMENSION);\n",
              "      for (var j = 0 ; j < neighbors ; j++) {\n",
              "        const idx = i * neighbors + j; \n",
              "        const nbr_idx = Math.round(bonds.neighbor_idx[idx]);\n",
              "\n",
              "        if (nbr_idx < i) {\n",
              "          var r_j = positions.subarray(nbr_idx * DIMENSION, (nbr_idx + 1) * DIMENSION);\n",
              "          vertex_count = push_bond(vertices, normals, vertex_count, r_i, r_j, 0.1); //bonds.diameter / 2.0);\n",
              "        }\n",
              "      }\n",
              "    }\n",
              "\n",
              "    GL.bindBuffer(GL.ARRAY_BUFFER, bonds.vertex_buffer);\n",
              "    GL.bufferData(GL.ARRAY_BUFFER, vertices, GL.DYNAMIC_DRAW);\n",
              "\n",
              "    GL.bindBuffer(GL.ARRAY_BUFFER, bonds.vertex_normal_buffer);\n",
              "    GL.bufferData(GL.ARRAY_BUFFER, normals, GL.DYNAMIC_DRAW);\n",
              "\n",
              "    GL.uniform1f(SHADER.uniform.use_position, 0.0);\n",
              "    GL.uniform1i(SHADER.uniform.use_global_size, 1);\n",
              "    GL.uniform1i(SHADER.uniform.use_global_color, 1);\n",
              "\n",
              "    return vertex_count;\n",
              "  }\n",
              "\n",
              "  BOND_C_TABLE = [];\n",
              "  BOND_S_TABLE = [];\n",
              "  for (var i = 0 ; i < BOND_SEGMENTS ; i++)\n",
              "  {\n",
              "    BOND_C_TABLE.push(Math.cos(2 * i * Math.PI / BOND_SEGMENTS));\n",
              "    BOND_S_TABLE.push(Math.sin(2 * i * Math.PI / BOND_SEGMENTS));\n",
              "  }\n",
              "\n",
              "  function push_bond(vertices, normals, vertex_count, r_i, r_j, radius) {\n",
              "    var dr = vec3.fromValues(r_i[0] - r_j[0], \n",
              "                             r_i[1] - r_j[1], \n",
              "                             r_i[2] - r_j[2]);\n",
              "\n",
              "    if (Math.abs(dr[0]) > BOX_SIZE[0] / 2.0 || \n",
              "        Math.abs(dr[1]) > BOX_SIZE[1] / 2.0 ||\n",
              "        Math.abs(dr[2]) > BOX_SIZE[2] / 2.0)\n",
              "      return vertex_count;\n",
              "\n",
              "    var up = vec3.fromValues(0.0, 1.0, 0.0);\n",
              "    var left = vec3.fromValues(0.0, 1.0, 0.0);\n",
              "\n",
              "    vec3.cross(left, up, dr);\n",
              "    vec3.normalize(left, left);\n",
              "\n",
              "    vec3.cross(up, left, dr);\n",
              "    vec3.normalize(up, up);\n",
              "\n",
              "    var normal = vec3.fromValues(0.0, 0.0, 0.0);\n",
              " \n",
              "    for (var i = 0 ; i < BOND_SEGMENTS ; i++) {\n",
              "      const c1 = radius * BOND_C_TABLE[i];\n",
              "      const c2 = radius * BOND_C_TABLE[(i + 1) % BOND_SEGMENTS];\n",
              "      const s1 = radius * BOND_S_TABLE[i];\n",
              "      const s2 = radius * BOND_S_TABLE[(i + 1) % BOND_SEGMENTS];\n",
              "\n",
              "      vertices.set([\n",
              "        r_j[0] + left[0] * c1 + up[0] * s1,\n",
              "        r_j[1] + left[1] * c1 + up[1] * s1,\n",
              "        r_j[2] + left[2] * c1 + up[2] * s1,\n",
              "\n",
              "        r_i[0] + left[0] * c1 + up[0] * s1,\n",
              "        r_i[1] + left[1] * c1 + up[1] * s1,\n",
              "        r_i[2] + left[2] * c1 + up[2] * s1,\n",
              "\n",
              "        r_j[0] + left[0] * c2 + up[0] * s2,\n",
              "        r_j[1] + left[1] * c2 + up[1] * s2,\n",
              "        r_j[2] + left[2] * c2 + up[2] * s2,\n",
              "\n",
              "        r_i[0] + left[0] * c1 + up[0] * s1,\n",
              "        r_i[1] + left[1] * c1 + up[1] * s1,\n",
              "        r_i[2] + left[2] * c1 + up[2] * s1,\n",
              "\n",
              "        r_i[0] + left[0] * c2 + up[0] * s2,\n",
              "        r_i[1] + left[1] * c2 + up[1] * s2,\n",
              "        r_i[2] + left[2] * c2 + up[2] * s2,\n",
              "\n",
              "        r_j[0] + left[0] * c2 + up[0] * s2,\n",
              "        r_j[1] + left[1] * c2 + up[1] * s2,\n",
              "        r_j[2] + left[2] * c2 + up[2] * s2,        \n",
              "      ], 3 * (vertex_count + 6 * i));\n",
              "\n",
              "      vec3.cross(normal, \n",
              "                 [r_j[0] - r_i[0] + left[0] * c1 + up[0] * s1, \n",
              "                  r_j[1] - r_i[1] + left[1] * c1 + up[1] * s1, \n",
              "                  r_j[2] - r_i[2] + left[2] * c1 + up[2] * s1],\n",
              "                 [left[0] * (c1 - c2) + up[0] * (s1 - s2), \n",
              "                  left[1] * (c1 - c2) + up[1] * (s1 - s2), \n",
              "                  left[2] * (c1 - c2) + up[2] * (s1 - s2)]);\n",
              "      vec3.normalize(normal, normal);\n",
              "\n",
              "      normals.set([\n",
              "        normal[0], normal[1], normal[2],\n",
              "        normal[0], normal[1], normal[2],\n",
              "        normal[0], normal[1], normal[2],\n",
              "        normal[0], normal[1], normal[2],\n",
              "        normal[0], normal[1], normal[2],\n",
              "        normal[0], normal[1], normal[2],\n",
              "      ], 3 * (vertex_count + 6 * i));\n",
              "    }\n",
              "    return vertex_count + 6 * BOND_SEGMENTS;\n",
              "  }\n",
              "\n",
              "  // SHADER CODE\n",
              "\n",
              "  const VERTEX_SHADER_SOURCE_2D = `#version 300 es\n",
              "    // Vertex Shader Program.\n",
              "    in vec2 vertex_position;\n",
              "    in vec2 position;\n",
              "    in float size;\n",
              "    in vec3 color;\n",
              "\n",
              "    out vec4 v_color;\n",
              "\n",
              "    uniform float use_position;\n",
              "\n",
              "    uniform vec2 screen_position;\n",
              "    uniform vec2 screen_size;\n",
              "\n",
              "    uniform float global_size;\n",
              "    uniform bool use_global_size;\n",
              "\n",
              "    uniform vec3 global_color;\n",
              "    uniform bool use_global_color;\n",
              "\n",
              "    void main() {\n",
              "      float _size = use_global_size ? global_size : size;\n",
              "      vec2 v = (_size * vertex_position + position - screen_position) / screen_size;\n",
              "      gl_Position = vec4(v, 0.0, 1.0);\n",
              "      v_color = vec4(use_global_color ? global_color : color, 1.0f);\n",
              "    }\n",
              "  `;\n",
              "\n",
              "  const FRAGMENT_SHADER_SOURCE_2D = `#version 300 es\n",
              "    precision mediump float;\n",
              "\n",
              "    in vec4 v_color;\n",
              "\n",
              "    out vec4 outColor;\n",
              "\n",
              "    void main() {\n",
              "      outColor = v_color;\n",
              "    }\n",
              "  `;\n",
              "\n",
              "   const VERTEX_SHADER_SOURCE_3D = `#version 300 es\n",
              "    // Vertex Shader Program.\n",
              "    in vec3 vertex_position;\n",
              "    in vec3 vertex_normal;\n",
              "\n",
              "    in vec3 position;\n",
              "    in float size;\n",
              "    in vec3 color;\n",
              "\n",
              "    out vec4 v_color;\n",
              "    out vec3 v_normal;\n",
              "\n",
              "    uniform mat4 perspective;\n",
              "\n",
              "    uniform float use_position;\n",
              "\n",
              "    uniform float global_size;\n",
              "    uniform bool use_global_size;\n",
              "\n",
              "    uniform vec3 global_color;\n",
              "    uniform bool use_global_color;\n",
              "\n",
              "    void main() {\n",
              "      vec3 pos = use_position * position;\n",
              "      float _size = use_global_size ? global_size : size;\n",
              "\n",
              "      vec3 v = (_size * vertex_position + pos);\n",
              "      gl_Position = perspective * vec4(v, 1.0);\n",
              "\n",
              "      v_color = vec4(use_global_color ? global_color : color, 1.0f);\n",
              "      v_normal = vertex_normal;\n",
              "    }\n",
              "  `;\n",
              "\n",
              "  const FRAGMENT_SHADER_SOURCE_3D = `#version 300 es\n",
              "    precision mediump float;\n",
              "\n",
              "    in vec4 v_color;\n",
              "    in vec3 v_normal;\n",
              "\n",
              "    uniform vec3 light_direction;\n",
              "\n",
              "    out vec4 outColor;\n",
              "\n",
              "    void main() {\n",
              "      float diffuse_magnitude = clamp(-dot(v_normal, light_direction), 0.0, 1.0) + 0.2;\n",
              "\n",
              "      outColor = vec4(vec3(v_color) * diffuse_magnitude, v_color.a);\n",
              "    }\n",
              "  `;\n",
              "\n",
              "  function initialize_shader(gl, vertex_shader_source, fragment_shader_source) {\n",
              "\n",
              "    const vertex_shader = compile_shader(\n",
              "      gl, gl.VERTEX_SHADER, vertex_shader_source);\n",
              "    const fragment_shader = compile_shader(\n",
              "      gl, gl.FRAGMENT_SHADER, fragment_shader_source);\n",
              "\n",
              "    const shader_program = gl.createProgram();\n",
              "    gl.attachShader(shader_program, vertex_shader);\n",
              "    gl.attachShader(shader_program, fragment_shader);\n",
              "    gl.linkProgram(shader_program);\n",
              "\n",
              "    if (!gl.getProgramParameter(shader_program, gl.LINK_STATUS)) {\n",
              "      alert(\n",
              "        'Unable to initialize shader program: ' + \n",
              "        gl.getProgramInfoLog(shader_program)\n",
              "        );\n",
              "        return null;\n",
              "    }\n",
              "    return shader_program;\n",
              "  }\n",
              "\n",
              "  function compile_shader(gl, type, source) {\n",
              "    const shader = gl.createShader(type);\n",
              "    gl.shaderSource(shader, source);\n",
              "    gl.compileShader(shader);\n",
              "\n",
              "    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n",
              "      alert('An error occured compiling shader: ' + gl.getShaderInfoLog(shader));\n",
              "      gl.deleteShader(shader);\n",
              "      return null;\n",
              "    }\n",
              "\n",
              "    return shader;\n",
              "  }\n",
              "\n",
              "  // UI\n",
              "\n",
              "  var DRAG_START;\n",
              "  var DRAG_CURRENT;\n",
              "  var DRAGGING = false;\n",
              "\n",
              "  CANVAS.addEventListener('mousedown', function(e) {\n",
              "    DRAG_START = [e.offsetX, e.offsetY];\n",
              "    DRAGGING = true;\n",
              "  });\n",
              "\n",
              "  CANVAS.addEventListener('mousemove', function(e) {\n",
              "    DRAG_CURRENT = [e.offsetX, e.offsetY];\n",
              "  });\n",
              "\n",
              "  CANVAS.addEventListener('mouseup', function(e) {\n",
              "    const delta = get_drag_offset();\n",
              "    if (DIMENSION == 2) {\n",
              "      SCREEN_POSITION = [SCREEN_POSITION[0] + delta[0],\n",
              "                         SCREEN_POSITION[1] + delta[1]];\n",
              "    } else if (DIMENSION == 3) {\n",
              "      YAW -= delta[0];\n",
              "      PITCH -= delta[1];\n",
              "\n",
              "      if (PITCH > Math.PI / 2.1)\n",
              "        PITCH = Math.PI / 2.1;\n",
              "      if (PITCH < -Math.PI / 2.1)\n",
              "        PITCH = -Math.PI / 2.1;\n",
              "\n",
              "      make_look_at();\n",
              "    }\n",
              "\n",
              "    DRAGGING = false;\n",
              "  });\n",
              "\n",
              "  function toggle_play() {\n",
              "    IS_PLAYING = !IS_PLAYING;\n",
              "    console.log(PLAY_PAUSE_BUTTON);\n",
              "    if(IS_PLAYING)\n",
              "      PLAY_PAUSE_BUTTON.innerHTML = '||';\n",
              "    else\n",
              "      PLAY_PAUSE_BUTTON.innerHTML = '>';\n",
              "  }\n",
              "\n",
              "  function change_frame(value) {\n",
              "    if (!IS_LOADED)\n",
              "      return;\n",
              "    CURRENT_FRAME = value;\n",
              "    FRAME_RANGE.innerHTML = value;\n",
              "  }\n",
              "\n",
              "  function get_drag_offset() {\n",
              "    var delta = [DRAG_START[0] - DRAG_CURRENT[0],\n",
              "                 -DRAG_START[1] + DRAG_CURRENT[1]];\n",
              "    delta = [delta[0] / canvas.width * 2, delta[1] / canvas.height * 2];\n",
              "    if (DIMENSION == 2) {\n",
              "      delta = [delta[0] * CAMERA_SIZE[0],\n",
              "               delta[1] * CAMERA_SIZE[1]];\n",
              "    }\n",
              "    return delta;\n",
              "  }\n",
              "\n",
              "  const SCALE_SPEED = 0.1;\n",
              "  CANVAS.addEventListener('mousewheel', function(e) {\n",
              "    var delta = Math.sign(e.wheelDeltaY);\n",
              "    if (navigator.appVersion.indexOf('Mac'))\n",
              "      delta *= -0.1;\n",
              "    if (DIMENSION == 2) {\n",
              "      CAMERA_SIZE[0] = CAMERA_SIZE[0] * (1 + delta * SCALE_SPEED);\n",
              "      CAMERA_SIZE[1] = CAMERA_SIZE[1] * (1 + delta * SCALE_SPEED);\n",
              "    } else if (DIMENSION == 3) {\n",
              "      VIEW_DISTANCE = VIEW_DISTANCE * (1 + delta * SCALE_SPEED);\n",
              "      make_look_at();\n",
              "    }\n",
              "    e.preventDefault();\n",
              "  }, false);\n",
              "  CANVAS.addEventListener('DOMMouseScroll', function(e) {\n",
              "    const delta = Math.sign(e.detail);\n",
              "    if (DIMENSION == 2) {\n",
              "      CAMERA_SIZE[0] = CAMERA_SIZE[0] * (1 + delta * SCALE_SPEED);\n",
              "      CAMERA_SIZE[1] = CAMERA_SIZE[1] * (1 + delta * SCALE_SPEED);\n",
              "    } else if (DIMENSION == 3) {\n",
              "      VIEW_DISTANCE = VIEW_DISTANCE * (1 + delta * SCALE_SPEED);\n",
              "      make_look_at();\n",
              "    }\n",
              "    e.preventDefault();\n",
              "  }, false);\n",
              "\n",
              "\n",
              "  // SERIALIZATION UTILITIES\n",
              "  function decode(sBase64, nBlocksSize) {\n",
              "    var chrs = atob(sBase64);\n",
              "    var array = new Uint8Array(new ArrayBuffer(chrs.length));\n",
              "\n",
              "    for(var i = 0 ; i < chrs.length ; i++) {\n",
              "      array[i] = chrs.charCodeAt(i);\n",
              "    }\n",
              "\n",
              "    return new Float32Array(array.buffer);\n",
              "  }\n",
              "\n",
              "  function from_json(data) { \n",
              "    data = data.data['application/json'];\n",
              "\n",
              "    if (typeof data == 'string') {\n",
              "      return JSON.parse(data);\n",
              "    }\n",
              "\n",
              "    return data;\n",
              "  }\n",
              "\n",
              "  // RUN CELL\n",
              "\n",
              "  load_simulation();\n",
              "  update_frame();\n",
              "</script>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8E59mHRb4-3R"
      },
      "source": [
        "Finally, let's plot the velocity distribution compared with its theoretical prediction."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SCKwEVc_5BEk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb16b593-439f-485e-9cad-2bb96fc9c191"
      },
      "source": [
        "V_flat = onp.reshape(onp.array(state.velocity), (-1,))\n",
        "occ, bins = onp.histogram(V_flat, bins=100, normed=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: VisibleDeprecationWarning: Passing `normed=True` on non-uniform bins has always been broken, and computes neither the probability density function nor the probability mass function. The result is only correct if the bins are uniform, when density=True will produce the same result anyway. The argument will be removed in a future version of numpy.\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9JJPIIq5DmG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "611ae855-d584-4176-df63-89528f2ae186"
      },
      "source": [
        "T_cur = kT(steps * dt)\n",
        "plt.semilogy(bins[:-1], occ, 'o')\n",
        "plt.semilogy(\n",
        "    bins[:-1], \n",
        "    1.0 / np.sqrt(2 * np.pi * T_cur) * onp.exp(-1/(2 * T_cur) * bins[:-1] ** 2), \n",
        "    linewidth=3)\n",
        "format_plot('t', 'T')\n",
        "finalize_plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}